Use of resonance inspection for process control

ABSTRACT

Generation of feedback for a part production process based on vibrational testing of parts produced by the part production process. A response characteristic may be identified from vibrational data regarding the parts that is correlated to a process variable of the part production process. The response characteristic may relate to a state of the process variable such that identification of the response characteristic may allow for generation of feedback regarding adjustment of a process control. Such response characteristic may relate to a vibrational metric regarding vibrational data and may comprise identifying a trend in data between a plurality of parts. Also presented are approaches to evaluation of parts, including batch evaluation of parts in which collective vibrational data regarding a plurality of parts belonging to a batch are analyzed. The process control aspects may be performed independently or in combination with part evaluation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application claims the benefit of U.S. 62/614,036 entitled“USE OF RESONANCE INSPECTION FOR PROCESS CONTROL,” and filed on Jan. 5,2017, the entirety of which is hereby incorporated by reference.

FIELD

The present disclosure relates to use of vibrational data for of parts,and in particular, to use of vibrational data for parts to assist inprocess control of a part production process used to produce parts.

BACKGROUND

Resonance inspection is a proven non-destructive method for theevaluation of stiff components. Resonance inspection may be provided byimpulse-induced vibration, swept-sine wave stimulation, and otheracoustic or ultrasonic techniques in which a part under test is vibratedand the vibrational response of the part is measured. Resonancemeasurements may be made with electronic listening devices, contacttransducers, laser interferometers, and other contact and non-contactmeans. Resonance data may be processed via fast Fourier Transforms(FFT), direct waveform measure, or other methods. Resonance inspectioncan provide quantifiable results from non-destructive test means thatmay be used to evaluate parts.

Part resonances are determined by the part's material properties,stiffness, and dimensions, all of which are typically tightly tied to apart's ability to perform as designed. When these material or geometricfeatures are changed in a manner that will affect part performance, dueto manufacturing defects or in-service damage, the resonances willreflect these changes, and the part can be discriminated from theacceptable population based on the resonance response of a part undertest.

SUMMARY

It is presently recognized that vibrational inspection of parts,including resonance inspection techniques, may provide meaningfulinformation that may be used to monitor, evaluate, and/or modify a partproduction process used to produce the parts. That is, while it has beenproposed that vibrational data be used to evaluate individual parts or apopulation of parts to determine if such parts are acceptable forperformance as designed, the present disclosure further contemplatesthat vibrational data regarding parts may be used for monitoring andevaluation of a part production process used to produce the parts. Inthis regard, it has been found that the behavior of the vibrationalresponse of a part that is vibrated (referred to herein as a vibrationalresponse characteristic or resonance response characteristic) may becorrelated to certain variables to provide information regarding suchvariables. Specifically, such variables may be related tocharacteristics of the part itself or characteristics of a process usedto produce the part. In either regard, the information gained regardingthese variables may be used to provide feedback to a part productionprocess used to produce parts. As such, information regarding theproduction process may be obtained by the vibrational inspection ofparts that may be used to improve the part production process to, forexample, make more consistent parts and/or reduce the number ofunacceptable parts produced by the process. For instance, after testingone or more parts, a response characteristic may be identified fromvibrational data acquired during the testing. The responsecharacteristic may be correlated to a process variable. The vibrationaldata, and specifically the response characteristic identified from thevibrational data, may provide feedback regarding a control for theprocess that may be used to adjust a control of the part productionprocess that controls the process variable. In turn, process feedbackmay be provided based on the vibrational data that may be used to modifyor otherwise control the part production process.

In various embodiments provided herein, parts may be evaluatedindividually or as a batch of parts. A batch of parts generally includesa plurality of parts. It may be appreciated that different evaluationsmay be made in relation to individual ones of the parts and batches ofparts. For instance, a response characteristic may be identified from anindividual part regarding a certain process variable. However, otherresponse characteristics related to other process variables may not beidentifiable in a single part. In turn, a batch of parts may becollectively analyzed to identify a response characteristic that mayprovide information regarding a part production process.

Moreover, certain information regarding a plurality of parts may beobtained from a batch analysis in which vibrational data regarding aplurality of parts are collectively analyzed. In this regard, thepresent disclosure describes evaluation of parts using a batch analysisin which vibrational data regarding a plurality of parts is analyzed.This batch analysis may be used in connection with a batch sort toevaluate the batch of parts (e.g., for classification of the parts) ascompliant or non-compliant parts. That is, even if individual ones ofthe parts within a batch of parts is classified as a compliant, theindividual ones of the parts may be classified as non-compliant based onthe batch analysis. This batch evaluation may allow for monitoring of acharacteristic of a part production process that may only beidentifiable from a plurality of parts may affect the compliance of suchparts. In turn, a batch analysis may be applied to a plurality of partsto determine compliance of such parts. Such batch analysis may beconducted in conjunction with a sort of individual parts or may beconducted as in independent test apart from any individualized testingof parts.

It may also be appreciated that the response characteristic identifiedfrom vibrational testing of one or more parts may be independent fromclassification of any one or more of the parts as compliant ornon-compliant in relation to a sort. That is, a response characteristicthat reflects a state of a process variable may be identified fromcompliant parts. Accordingly, such a response characteristic may evenindicate that a correlated process variable requires action to modify acontrol of a process variable where the part is compliant. This may bevaluable in that vibrational testing to identify a responsecharacteristic from one or more parts may provide feedback to a partproduction process before the parts produced by the part productionprocess (or a statistically significant number of parts produced) becomenon-compliant. For instance, the vibrational testing may allow foridentification of a trend among a response characteristic that iscorrelated to a process variable. Identification of such a trend in aresponse characteristic may allow for adjustment of a process control toaddress the trend in the response characteristic (e.g., to reverse atrend indicating the process is trending out of control) before thevibrational data regarding the parts being produced by the partproduction process are classified as non-compliant parts. In turn, thevibrational analysis described herein may allow for process control of apart production process prior to the part production process trendingout of control to produce non-compliant parts.

As may be appreciated from the foregoing, having the ability to identifya process variable that correlates to a response characteristic mayfacilitate the process control feedback described herein. As such, thepresent disclosure includes approaches that allow for correlation ofvibrational data regarding parts produced by a part production processto process variables regarding the part production process. As will bedescribed in greater detail below, this correlation process may includeidentifying or calculating one or more metrics from the vibrationaldata. Specifically, in some contexts of the present disclosure, thevibrational testing may comprise resonance inspection and the resultingvibrational data may be resonance response data. As such, one or moreresonance metrics may be identified or calculated regarding theresonance response data. In particular, the metric may include, but isnot limited to, a multi-frequency analysis. This multi-frequencyanalysis may include combining multiple frequency measures and/orgenerating further metrics therefrom. In any regard, the metrics fromthe vibrational data may be analyzed in relation to changes in one ormore process variables used to produce parts to identify a correlationbetween a response characteristic in the vibrational data (that mayinclude or relate to a metric from the data) and a process variable.Identification of such correlation between a response characteristic anda process variable may allow for control over the process variable inresponse to the response characteristic as recognized from vibrationaldata regarding a part produced by a part production process.

Accordingly, a first aspect of the present disclosure may becharacterized as a method for control of a part production process. Themethod includes acquiring vibrational data for at least a first partproduced by the part production process. As will be appreciated in thedescription below, such acquiring may include performing testing of thefirst part or simply accessing the data from a previously populated datastore. The vibrational data includes a frequency response of the firstpart when excited at a plurality of input frequencies. The methodfurther includes identifying a response characteristic from thevibrational data. The response characteristic identified from thevibrational data is correlated to a process variable of the partproduction process. The method further includes determining a state ofthe process variable of the part production process based on theresponse characteristic correlated to the process variable and adjustinga control of the part production process related to the process variablein response to the state of the process variable.

A number of feature refinements and additional features are applicableto the first aspect. These feature refinements and additional featuresmay be used individually or in any combination. As such, each of thefollowing features that will be discussed may be, but are not requiredto be, used with any other feature or combination of features of thefirst aspect.

For instance, the method of the first aspect may be used in conjunctionwith a resonance testing system that may be operative to measure aresonance response of a part (e.g., to acquire the vibrational data).The vibrational data may be resonance response data of the first part.This resonance response data may be gathered in response to the excitingthe part at the plurality of frequencies. In this regard, the responsecharacteristic may correspond to a resonance metric based on theresonance response data. As will be discussed in greater detail below,whether from resonance data or other vibrational data, such a metric mayinclude any characteristic of the vibrational data including magnitudes,frequencies, relationships between resonance modes, or any otherappropriate characterization of the vibrational data.

In an embodiment, the method of the first aspect may include acquiringvibrational data for a plurality of parts produced by the partproduction process. In this regard, the response characteristic may beat least based on a change in the vibrational data between differentones of the plurality of parts (e.g., relative to the order in which theparts are produced). That is, rather than identifying a resonanceresponse in any given one part, the method may include monitoring aplurality of parts to detect a change in vibrational data between theparts that is indicative of a response characteristic that is correlatedto a process variable. The change in the vibrational data betweendifferent ones of the plurality of parts may include a trend in theresponse characteristic measured over a given number of the plurality ofparts relative to the order in which the parts were produced or a givenperiod of time over which the parts are produced. That is, the state ofthe process variable may be determined based on identification of atrend in the response characteristic. A trend may be characterized as aprogression of a value in a single direction over the given number ofparts or the given period of time. The value that is subject to thetrend may correspond directly to the value of the data or somecharacteristic or metric of the data such as, for example, a valuerelated to the variability of the data. In this regard, a progression ofthe variability of the data (e.g., a contraction or expansion of thevariability of the data) over a given number of parts may comprise atrend. Such a progression of the value may be constant over the givennumber of parts or given period of time such that each subsequent valuerepresents a continual progression in the single direction over thegiven number of parts or given period of time, or the progression may becharacterized as some statistical representation of the values of partsover the given number of parts or given period of time. In this latterregard, not every subsequent part within the given number of parts orwithin the given period of time need progress in the single direction,but the statistical representation (e.g., a moving average or the like)over a given number of parts or a given period of time may move in thesingle direction.

In any regard, the given number of parts over which a trend may beidentified may vary in various embodiments of the method of the firstaspect. For instance, in the case where a trend is identified over thegiven number of parts, the given number of the plurality of parts may bebased on a production rate of the part production process. In anembodiment, the given number of the plurality of parts may be at leastabout 5, at least about 10, at least about 25, at least about 50, atleast about 100, at least about 250, at least about 500, or at leastabout 1,000. In addition, the given number of parts may be not more thanabout 5,000, not more than about 2,500, not more than 2,000, not morethan about 1,000, not more than about 100, not more than about 75, notmore than 50, or not more than about 25, provided that the upper boundon the given number of parts is greater than the lower bound on thegiven number of parts.

Alternatively, a trend may be identified over a given period of time inwhich parts are produced. In this regard, the change in the vibrationaldata between different respective ones of the plurality of parts mayinclude a trend in the response characteristic measured over a giventime period in which the plurality of parts are produced. The given timeperiod may be based on a production rate of the part production process.In various embodiments, the given period of time may be at least about 1minute (0.017 hours), at least about 5 minutes (0.083 hours), at leastabout 10 minutes (0.167 hours), at least about 15 minutes (0.25 hours),at least about 30 minutes (0.5 hours), at least about 1 hour, or atleast about 2 hours. In addition, the given period of time may be notmore than about 8 hours, not more than about 5 hours, not more than 3hours, or not more than about 1 hour, not more than about 45 minutes(0.75 hours), not more than about 30 minutes (0.5 hours), not more thanabout 15 minutes (0.25 hours), or not more than about 10 minutes (0.167hours), provided that the upper bound on the given period of time isgreater than the lower bound on the period of time.

The method of the first aspect may also include evaluating the firstpart to determine if the part is compliant or acceptable for itsintended use, in addition to analysis of the vibrational data todetermine the state of a process variable. For instance, the method mayalso include testing the frequency response of the first part against asort. The sort may be based upon vibrational data from a qualificationpopulation of parts. In this regard, the sort may include at least oneboundary relative to the frequency response defining at least one of acompliant part classification sort result or a non-compliant partclassification sort result. That is, the sort may be operative toclassify the first part into one of a compliant part classification or anon-compliant part classification based on the vibrational data of apart in relation to the at least one boundary.

As may be appreciated, detecting that a process variable should beadjusted may be especially beneficial if the detection of the responsecharacteristic indicative that the process variable should be adjustedmay be detected prior to the part production process actually producinga statistically significant number of defective parts or parts thatwould be classified as non-compliant. In this regard, the method of thefirst aspect may include adjusting the control prior to a change in thevibrational data between ones of the plurality of parts resulting in anon-compliant part classification sort result for a part of theplurality of parts produced by the part production process. That is, asparts are analyzed to identify a response characteristic in thevibrational data for the parts, the response characteristic may beidentified that indicates a process variable is to be adjusted prior tothe vibrational data indicating that any given part or a statisticallysignificant number of parts is non-compliant. In short, the responsecharacteristic may be identified that indicates a process variableadjustment from compliant parts so that the process variable may beadjusted prior to the part production process trending out of control orproducing non-compliant parts. As an example, the state of the processvariable may be determined based on the response characteristic hasexceeded a limit established in relation to the response characteristic.The limit may be established relative to a boundary within thevibrational data between compliant parts and non-compliant parts suchthat the limit may be met prior to the boundary being met by parts.

As described above, the state of the process variable may be determinedbased on the response characteristic that is correlated to the processvariable. As an example, the response characteristic may indicate thatthe process variable is in a state that is preferably adjusted to returnthe state of the process variable into or toward a controlled condition.Not only can the state of the process variable that is determined basedon the response characteristic indicate that the process variable is tobe adjusted, but the state of the process variable may also provideinformation that may be used in the adjusting of the control of theprocess variable. For instance, the state of the process control may beindicative of a magnitude of adjustment of the control and/or adirection of adjustment of the control. That is, because the responsecharacteristic may be correlated to the process variable, the responsecharacteristic may provide that that allows the state of the processvariable to be determined such that either or both of a direction ofadjustment of the control and/or the magnitude of adjustment may beprovided. In this regard, the correlation between the responsecharacteristic and the process variable may include a quantification ofthe relationship between the response characteristic and the processvariable such that identification of the response characteristic mayallow for determination of a quantity by which the process variable isto be adjusted (including the direction and/or magnitude of control).

In an embodiment of the method of the first aspect, the method may alsoinclude correlating the response characteristic from the vibrationaldata to the process variable. That is, the method may includedetermining the correlation between the response characteristic and theprocess variable. The correlating may include acquiring vibrational datafor a plurality of parts produced by the part production process andobtaining respective different process variable values for the processvariable for each of the plurality of parts. Specifically, therespective process variable values may differ with regard to theplurality of parts. This range of values for the process variable usedto produce the plurality of parts may result in the plurality of partshaving varying vibrational data resulting from the variation in theprocess variable to facilitate the correlation between the responsecharacteristic of the vibrational data and the process variable. Inturn, the method may include identifying a correlation between theprocess variable and the vibrational data.

Various approaches to determining the correlation may be provided. Forinstance, the identifying the correlation may include evaluationrelative to a multidimensional data set in which each of a plurality ofvibration data metrics comprise corresponding respective metricdimensions in the multidimensional data set and the process variablecomprises a non-vibrational dimension in the multidimensional data set.In an embodiment, the correlation includes evaluating a plurality ofmetric dimensions relative to the non-vibrational dimension to determinethe correlation between a given metric dimension and the non-vibrationaldimension. The evaluating may include at least one of a classificationanalysis or a regression analysis that may identify and/or quantify acorrelation between one or more of the vibrational dimensions relativeto the non-vibrational dimensions. As way of example, the evaluating mayinclude at least one of a non-linear least squares regression, acorrelation coefficient analysis, an analysis of variance (ANOVA),k-means clustering, principal components analysis, or random forestanalysis.

As addressed above, the method of the first aspect may include testingthe first part to obtain the vibrational data used in the method. Inthis regard, the method may include exciting the first part at theplurality of input frequencies and measuring the frequency response ofthe first part. In addition, the method may include generating thevibrational data for the first part based on the measured frequencyresponse of the first part. In this regard, the vibrational data mayinclude raw vibrational response data and/or one or more metricsgenerated based on the raw vibrational response data. A metric maydefine any value, relative relation, statistical representation,mathematical transformation, or combinations thereof that are generatedfrom the raw vibrational data. Examples of metrics may includeamplitudes of frequency responses, relative positions of resonance peakswithin the frequency response, mathematical transformations such as FastFourier transforms (FFTs), or any other appropriate metric generatedfrom the response data.

The response characteristic identified from the vibrational data mayallow for a wide range of one or more process variables to beeffectively monitored through inspection of the vibrational data. Forinstance, in an embodiment, the process variable may include amanufacturing variable. The manufacturing variable may relate to one ormore manufacturing process steps or equipment used in the partproduction process. Examples of manufacturing variables include, but arenot limited to, a process temperature, a process rate, manufacturingcomponent wear, or a raw material property. In an embodiment, theprocess variable may relate to a component variable. That is, theprocess variable may relate to a resulting quality of the part that isproduced by the manufacturing process. In at least some instances, thecomponent variable may result from the process variable. That is, theresponse characteristic may be correlated to a component variable thatresults from another process variable (e.g., a manufacturing variable).As an example, a root cause of a change in the vibrational data may bethe wear of a mold used in the part production process. The wear of themold may cause resulting parts produced by the wearing mold to change indimension. The change in dimension may result in a change in thevibrational data in the parts having a growing dimension, which in turnis a function of the wear of the mold. As such, the responsecharacteristic may be the change in the vibrational data that indicatesthe change in the part dimension, which in turn is correlated to thewear of the mold. In any regard, the component variable may include, butis not limited to, a part dimension, a stress state, a crystallographicorientation, a material property, phase ratios, part chemistry, or partmicrostructure.

As will also be discussed in greater detail below, a plurality of partsmay belong to a batch of parts that may be used for various aspectsdescribed herein. For instance, the acquiring may include acquiringcollective vibrational data regarding a plurality of parts produced bythe production part process and the response characteristic may includea metric from the collective vibrational data. As discussed in greaterdetail below, such a response characteristic determined from thecollective vibrational data may include a characteristic of thecollective vibrational data or may include a change in the collectivevibrational data over a plurality of batches.

A second aspect of the present invention may be characterized as a toolfor controlling a part production system that performs a part productionprocess. The tool may comprise a data store comprising vibrational datafor at least a first part produced by the part production process. Thevibrational data includes a frequency response of the first part whenexcited at a plurality of input frequencies. The tool also includes acorrelation analysis module in operative communication with the datastore to retrieve the vibrational data. The correlation analysis moduleis operative to identify a response characteristic from the vibrationaldata that is correlated to a process variable of the part productionprocess and determine a state of the process variable of the partproduction process based on the response characteristic correlated tothe process variable. In turn, the tool includes a control module thatis in operative communication with a control of the part productionprocess that controls the process variable. The control module isoperative to determine an adjustment of the control of the partproduction process related to the process variable in response to thestate of the process variable.

A number of feature refinements and additional features are applicableto the second aspect. These feature refinements and additional featuresmay be used individually or in any combination. As such, each of thefollowing features that will be discussed may be, but are not requiredto be, used with any other feature or combination of features of thesecond aspect.

For instance, the tool of the second aspect may be used to perform anyor all of the features of the method of the first aspect. In thisregard, any feature described above in relation to the method of thefirst aspect may be applicable to and/or performed by the tool of thesecond aspect.

In addition, the tool of the second aspect may include one or moremodules operative to perform a specified function. Any of the modulesdescribed in the tool of the second aspect or in any other portion ofthis disclosure may comprise any appropriate hardware or softwarecomponents to perform in the manner described in relation to such amodule. In this regard, a module may include one or more hardwarecomponents including, for example, a field programmable gate array, anapplication specific integrated circuit, or other hardware component.Additionally or alternatively, a module may be implemented usingsoftware. As such, reference to a module may include correspondingcomputer hardware for execution of the module including one or moreprocessors that may be in operative communication with a physical memorydevice. Specifically, the one or more processors may retrieveinstructions comprising non-transitory machine-readable instructionsthat may be stored digitally on the physical memory device. In turn, theinstructions, when executed by the processor, may configure theprocessor to perform the functionality described in relation to themodule. Additional computer hardware may be provided to facilitateoperation of the processor including busses, networking components, orthe like, and may be included as part of a module.

In an embodiment, the vibrational data comprises resonance response dataand the response characteristic corresponds to a resonance metric basedon the resonance response data. The vibrational data may be for aplurality of parts produced by the part production process, and theresponse characteristic may be at least based on a change in thevibrational data between different ones of the plurality of parts.

In an embodiment, the change in the vibrational data between differentones of the plurality of parts may include a trend in the responsecharacteristic measured over a given number of the plurality of partsrelative to the order in which the parts were produced. The given numberof the plurality of parts may be at least 10 parts. In otherembodiments, the given number of parts may be recording to any of thevalues described above in relation to the first aspect. Also, the givennumber of the plurality of parts may be based on a production rate ofthe part production process.

In another embodiment, the change in the vibrational data betweendifferent respective ones of the plurality of parts comprises a trend inthe response characteristic measured over a given time period in whichthe plurality of parts are produced. The given time period may be atleast 0.5 hours. In other embodiments, the given time period may berecording to any of the values described above in relation to the firstaspect. Also, the given time period may be based on a production rate ofthe part production process.

The tool of the second aspect may also include a vibrational testingsystem for testing the frequency response against a sort. The sort maybe based upon vibrational data from a qualification population. In thisregard, the sort may include at least one limit relative to thefrequency response defining at least one of a compliant partclassification sort result or a non-compliant part classification sortresult. As described above in relation to the first aspect, it may bedesirable to control the part production process prior to producingparts that are categorized as non-compliant. Accordingly, the controlmodule may adjust the control prior to a change in the vibrational databetween ones of the plurality of parts resulting in a non-compliant partclassification sort result for a part of the plurality of parts producedby the part production process.

In an embodiment, the state of the process variable may be determined bythe correlation analysis module based on the response characteristic hasexceeded a limit established in relation to the response characteristic.The state of the process variable may be determined by the correlationanalysis module based on identification of a trend in the responsecharacteristic. The state of the process control may be indicative of amagnitude of adjustment of the control. Additionally, the state of theprocess control may be indicative of a direction of adjustment of thecontrol.

In an embodiment, the correlation analysis module may be operative tocorrelate the response characteristic from the vibrational data to theprocess variable. The correlation analysis module may be operative toacquire vibrational data for a plurality of parts produced by the partproduction process, obtain respective process variable values for theprocess variable for each of the plurality of parts, and identify acorrelation between the process variable and the vibrational data. Therespective process variable values differ with regard to the pluralityof parts. The correlation analysis module may be operative to evaluate amultidimensional data set in which each of a plurality of vibrationalmetrics comprise corresponding respective metric dimensions in themultidimensional data set and the process variable comprises anon-vibrational dimension in the multidimensional data set. Thecorrelation analysis module may be operative to evaluate a plurality ofvibrational metric dimensions relative to the non-vibrational dimensionto determine the correlation between a given vibrational metricdimension and the non-vibrational dimension. In this regard, thecorrelation analysis module may perform at least one of a classificationanalysis or a regression analysis. The correlation analysis module mayperform at least one of a non-linear least squares regression, acorrelation coefficient analysis, an analysis of variance (ANOVA),k-means clustering, principal components analysis, or random forestanalysis.

In an embodiment, the vibrational testing system may be operative toexcite the first part at the plurality of input frequencies and measurethe frequency response of the first part. The vibrational testing systemmay further operative to generate the vibrational data for the firstpart based on the measured frequency response of the first part.

In an embodiment of the tool of the second aspect, the process variablemay include a manufacturing variable. The manufacturing variable mayinclude at least one of a process temperature, a process rate,manufacturing component wear, or a raw material property. The processvariable may relate to a component variable. The component variable mayresult from the process variable. The component variable may include atleast one of a part dimension, a stress state, a crystallographicorientation, a material property, phase ratios, part chemistry, or partmicrostructure.

As described above, the vibrational data may include vibrational datafrom a batch of parts for use in a batch evaluation approach. Forinstance, the vibrational testing system may be operative to acquirecollective vibrational data regarding a plurality of parts produced bythe production part process and the response characteristic may includea metric from the collective vibrational data.

A third aspect of the present disclosure may be characterized as amethod for evaluation of a plurality of parts based on vibrational datafor individual parts and collective vibrational data for a batch ofparts. This method may include acquiring collective vibrational data fora plurality of parts of a first production batch. The collectivevibrational data includes a frequency response of individual ones of theplurality of parts when excited at a plurality of input frequencies. Themethod may include comparing the collective vibrational data regardingthe first production batch relative to a batch sort that collectivelyevaluates an entirety of the first production batch and determiningwhether the first production batch satisfies a batch threshold relativeto the entirety of the first production batch based on the batch sort.

A number of feature refinements and additional features are applicableto the third aspect. These feature refinements and additional featuresmay be used individually or in any combination. As such, each of thefollowing features that will be discussed may be, but are not requiredto be, used with any other feature or combination of features of thethird aspect.

For instance, in an embodiment, the batch threshold may relate to atotal variation of the collective vibrational data for the plurality ofparts of the first production batch. That is, the total amount ofvariation in the vibrational data (e.g., for one or more metrics of thevibration data) for a batch may be determined in relation to theindividual vibrational data for given ones of the plurality of partsthat comprise the batch of parts. As described below, the vibrationaldata for individual ones of the plurality of parts may be discounted ornot considered in the collective vibrational data such that thecollective vibrational data that is evaluated against the batchthreshold need not include all of the vibrational data for eachindividual one of the plurality of parts of the batch. In otherembodiments, the batch threshold may relate to a change in thecollective vibrational data between the first production batch andanother production batch (e.g., a given production batch may be comparedto one or more prior batches).

The plurality of parts of the first production batch may correspond to abatch production process in which the plurality of parts arecollectively produced in the batch production process. Alternatively,the plurality of parts of the first production batch may correspond to agiven number of parts sequentially produced in a continuous productionprocess. In this regard, the given number of parts in the batch of partsmay be based on a production rate of the part production process. In anembodiment, the given number of the plurality of parts may be at leastabout 5, at least about 10, at least about 25, at least about 50, atleast about 100, at least about 250, at least about 500, or at leastabout 1,000. In addition, the given number of parts may be not more thanabout 5,000, not more than about 2,500, not more than 2,000, not morethan about 1,000, not more than about 100, not more than about 75, notmore than 50, or not more than about 25, provided that the upper boundon the given number of parts is greater than the lower bound on thegiven number of parts.

Alternatively, the plurality of parts of the first production batch maycorrespond to a number of parts produced over a given time period in acontinuous production process. The given time period may be based on aproduction rate of the part production process. In any regard, the givenperiod of time may be at least about 1 minute (0.017 hours), at leastabout 5 minutes (0.083 hours), at least about 10 minutes (0.167 hours),at least about 15 minutes (0.25 hours), at least about 30 minutes (0.5hours), at least about 1 hour, or at least about 2 hours. In addition,the given period of time may be not more than about 8 hours, not morethan about 5 hours, not more than 3 hours, or not more than about 1hour, not more than about 45 minutes (0.75 hours), not more than about30 minutes (0.5 hours), not more than about 15 minutes (0.25 hours), ornot more than about 10 minutes (0.167 hours), provided that the upperbound on the given period of time is greater than the lower bound on theperiod of time.

In an embodiment, the collective vibrational data may include astatistical representation of the vibrational data for the individualones of the plurality of parts of the first production batch. Forinstance, the statistical representation of the vibrational data mayinclude a standard deviation of the collective vibrational data.However, other statistical representations (e.g., mean, median, etc.)may be utilized without limitation.

The method of the third aspect may also include evaluation of individualones of the plurality of parts. In this regard, the method may alsoinclude acquiring vibrational data for at least a first part from thefirst production batch. The plurality of parts may include the firstpart such that the first part is included in the batch. The method mayfurther include testing the vibrational data for the first part againsta sort. The sort may be based upon vibrational data from a qualificationpopulation of parts. In turn, the method may include assigning the firstpart to one of a compliant part classification or a non-compliant partclassification based on the sort. The vibrational data for the firstpart may be assigned to the non-compliant classification and, asdescribed above, may be discounted in relation to the collectivevibrational data. That is, outlier parts that are classified asnon-compliant, but that belong to the batch of parts may be discountedor not considered at all in the collective vibrational data.

As will be described in greater detail below, a batch of parts notsatisfying the batch threshold may result in a number of possibleoutcomes. For instance, the plurality of parts comprising the firstproduction batch may undergo additional testing based on the collectivevibrational data failing to satisfy the batch threshold. Additionally oralternatively, the plurality of parts comprising the first productionbatch may be assigned to a non-compliant classification based on thecollective vibrational data failing to satisfy the batch threshold.

The analysis of the collective vibrational data of the batch of partsmay also allow for process feedback similar to the method of the firstaspect. In this regard, the method of the third aspect may includeidentifying a batch response characteristic from the collectivevibrational data. The batch response characteristic may be correlated toa first process variable of a part production process. The determiningmay include determining a state of the first process variable of thepart production process based on the batch response characteristiccorrelated to the first process variable. In turn, the method may alsoinclude adjusting a first process control associated with the firstprocess variable of the part production process used to produce theplurality of parts of the first production batch based on thedetermining. The adjusting may be at least partially based on the stateof the first process variable.

In addition, the method may include process feedback based on both thecollective vibrational data of the batch of parts as well as individualvibrational data for individual ones of the plurality of partscomprising the batch of parts. Accordingly, the method may includeidentifying a part response characteristic from vibrational data ofindividual ones of the plurality of parts. The part responsecharacteristic may be correlated to a second process variable of thepart production process, where the first process variable related to thebatch response characteristic is different than the second processvariable. The determining may include determining a state of the secondprocess variable of the part production process based on the partresponse characteristic correlated to the second process variable. Inturn, the method may also include adjusting a second process controlassociated with the second process variable of the part productionprocess used to produce the plurality of parts of the first productionbatch based on the determining. In this regard, the method may includemonitoring a response characteristic of the collective vibrational datafor the batch of parts as well as a part response characteristic of thevibrational data of individual ones of the plurality of parts to monitordifferent process variables that are correlated to the batch responsecharacteristic and the part response characteristic, respectively.

As described in relation to the first aspect, it may be preferable toidentify the response characteristic (e.g., the part responsecharacteristic or the batch response characteristic) prior to either thebatch or individual ones of the parts failing to satisfy a batchthreshold or being categorized as non-compliant based on a sort. Assuch, the adjusting may occur in response to the determining in whichthe first production batch satisfies the batch threshold. That is, anadjustment may occur even if the batch satisfies the batch threshold. Assuch, the adjusting may occur in response to a trend identified in aresonance metric of the collective vibrational data. The adjusting mayoccur prior to the resonance metric exceeding a limit defining anon-compliant part. Alternatively, the adjusting may occur in responseto the determining in which the first production batch does not satisfythe batch threshold. That is, the adjusting may occur in the instancethat the batch does not satisfy the batch threshold.

As may be appreciated, the first or the second process variabledescribed herein in relation to the third aspect may include any of theprocess variables described above in relation to the first or secondaspect. Further still, like in the first aspect, the vibrational data ofthe individual ones of the plurality of parts and/or the collectivevibrational data may comprise a resonance metric. In addition, themethod of the third aspect may include performing the vibrationaltesting on the parts such that the method includes exciting each of theplurality of parts at the plurality of input frequencies and measuringthe frequency response of the each of the plurality of parts. In turn,the method may also include generating vibrational data for each of theplurality of first parts based on the measured frequency response ofeach respective one of the plurality of first parts. Further still, themethod may include generating the collective vibrational data based onthe vibrational data for each respective one of the plurality of firstparts.

A fourth aspect may be characterized as a tool for evaluation of aplurality of parts based on vibrational data for individual parts andcollective vibrational data for a plurality of parts. The tool includesa data store comprising collective vibrational data for a plurality ofparts of a first production batch. The collective vibrational dataincludes a frequency response of individual ones of the plurality ofparts when excited at a plurality of input frequencies. The tool alsoincludes a batch evaluation module in operative communication with thedata store to access the collective vibrational data. The batchevaluation module is operative to compare the collective vibrationaldata regarding the first production batch relative to a batch sort thatcollectively evaluates an entirety of the first production batch anddetermining whether the first production batch satisfies a batchthreshold relative to the entirety of the first production batch basedon the batch sort.

A number of feature refinements and additional features are applicableto the fourth aspect. These feature refinements and additional featuresmay be used individually or in any combination. As such, each of thefollowing features that will be discussed may be, but are not requiredto be, used with any other feature or combination of features of thefourth aspect.

For instance, the tool of the fourth aspect may be used to perform anyor all of the features of the method of the third aspect. In thisregard, any feature described above in relation to the method of thethird aspect may be applicable to and/or performed by the tool of thefourth aspect.

Specifically, in an embodiment, the batch threshold may relate to atotal variation of the collective vibrational data for the plurality ofparts of the first production batch. Additionally or alternatively, thebatch threshold may relate to a change in the collective vibrationaldata between the first production batch and another production batch.

In an embodiment, the plurality of parts of the first production batchmay correspond to a batch production process in which the plurality ofparts are collectively produced in the batch production process.Alternatively, the plurality of parts of the first production batch maycorrespond to a given number of parts sequentially produced in acontinuous production process. For example, the given number of partsmay be at least 10 parts or may be according to the given number ofparts described above in relation to the first aspect. The given numberof parts may be based on a production rate of the part productionprocess. In still other embodiments, the plurality of parts of the firstproduction batch may correspond to a number of parts produced over agiven time period in a continuous production process. For example, thegiven time period may be at least 0.5 hours, or may be according to thegiven time period described above in relation to the first aspect. Thegiven time period may be based on a production rate of the partproduction process.

The collective vibrational data may include a statistical representationof the vibrational data for the individual ones of the plurality ofparts of the first production batch. For example, the statisticalrepresentation of the vibrational data comprises a standard deviation ofthe collective vibrational data. In other examples like those describedabove, any other appropriate statistical representation, mathematicaloperation, or any other metric may be applied to the collectivevibrational data.

The tool may include a vibrational testing system that is operative toacquire vibrational data for at least a first part from the firstproduction batch. The plurality of parts may include the first part, andthe vibrational data includes the frequency response of the first partwhen excited at the plurality of input frequencies. The vibrationaltesting system may be further operative to test the vibrational data forthe first part against a sort and assign the first part to one of acompliant part classification or a non-compliant part classificationbased on the sort. The sort may be based upon vibrational data from aqualification population of parts. In this regard, the vibrational datafor the first part may be assigned to the non-compliant classificationand may be discounted in relation to the collective vibrational data.

Various responses to a batch failing to satisfy the batch threshold maybe provided. For instance, the plurality of parts comprising the firstproduction batch may undergo additional testing based on the collectivevibrational data failing to satisfy the batch threshold. Additionally oralternatively, the plurality of parts comprising the first productionbatch may be assigned to a non-compliant classification by the batchevaluation module based on the collective vibrational data failing tosatisfy the batch threshold.

In an embodiment, the tool of the fourth aspect may include acorrelation analysis module operative to identify a batch responsecharacteristic from the collective vibrational data. The batch responsecharacteristic may be correlated to a first process variable of a partproduction process, and the correlation analysis module may determine astate of the first process variable of the part production process basedon the batch response characteristic correlated to the first processvariable. In addition, the tool may include a control module operativeto adjust a first process control associated with the first processvariable of the part production process used to produce the plurality ofparts of the first production batch based on the determining. Thecontrol module may adjust the control at least partially based on thestate of the first process variable. The control module may also adjustthe control in response to the batch evaluation module determining thefirst production batch satisfies the batch threshold. Specifically, thecontrol module may adjust the control in response to a trend identifiedin a resonance metric of the collective vibrational data by the batchevaluation module. The control module may adjust the control prior tothe resonance metric exceeding a boundary defining a non-compliant part.In other embodiments, the control module may adjust the control inresponse to the batch evaluation module determining the first productionbatch does not satisfy the batch threshold.

The correlation analysis module may also be operative to identify a partresponse characteristic from vibrational data of individual ones of theplurality of parts. The part response characteristic may be correlatedto a second process variable of the part production process differentthan the first process variable. That is, different process variablesmay be monitored using batch analysis and individual part analysis,respectively. In any regard, the correlation analysis module may beoperative to determine a state of the second process variable of thepart production process based on the part response characteristiccorrelated to the second process variable. A control module of the toolmay be operative to adjust a second process control associated with thesecond process variable of the part production process used to producethe plurality of parts of the first production batch based on the secondstate.

In an embodiment, at least one of the first process variable or thesecond process variable may comprise a manufacturing variable. Themanufacturing variable may include at least one of a processtemperature, a process rate, manufacturing component wear, or a rawmaterial property. At least one of the first process variable or thesecond process variable may additionally or alternatively include acomponent variable. The component variable may result from the processvariable. The component variable may include at least one of a partdimension, a stress state, a crystallographic orientation, a materialproperty, phase ratios, part chemistry, or part microstructure.

In an embodiment, the vibrational data may include a resonance metric.The tool may also include a vibrational testing system that is operativeto excite each of the plurality of parts at the plurality of inputfrequencies and measure the frequency response of the each of theplurality of parts. The vibrational testing system may be operative togenerate vibrational data for each of the plurality of first parts basedon the measured frequency response of each respective one of theplurality of first parts. The vibrational testing system may beoperative to generate the collective vibrational data based on thevibrational data for each respective one of the plurality of firstparts.

A fifth aspect may be characterized as a method for monitoring a partproduction process using vibrational data regarding parts produced bythe part production process. The method includes acquiring vibrationaldata for each of a plurality of parts produced by the part productionprocess. The vibrational data includes a frequency response of each ofthe plurality of parts when excited at a plurality of input frequencies.The method also includes generating a resonance metric from thevibrational data for each of the plurality of parts that is correlatedto a process variable of the part production process. In turn, theresonance metric is monitored over the plurality of parts produced bythe part production process to identify a trend in the resonance metricrelative to the sequence of production of the plurality of parts. Themethod also includes identifying a change in the process variableassociated with the trend.

A number of feature refinements and additional features are applicableto the fifth aspect. These feature refinements and additional featuresmay be used individually or in any combination. As such, each of thefollowing features that will be discussed may be, but are not requiredto be, used with any other feature or combination of features of thefifth aspect.

For instance, the trend may be indicative of a control state of theprocess variable. In turn, the method may also include determining anadjustment or actually adjusting a control of the part productionprocess related to the process variable in response to the state of theprocess variable. The process variable may be any of the processvariables described above in relation to the first aspect or the thirdaspect.

Moreover and as described above, the adjusting of the control of theprocess variable may occur prior to the resonance metric exceeding alimit relative to the resonance metric. Specifically, the limit maydefine at least one of a compliant part classification sort result or anon-compliant part classification sort result. In an embodiment, thelimit may be a bound on the resonance metric within a compliant partclassification sort result. In this regard, the identification of thetrend may allow a process variable to be adjusted prior to non-compliantparts being produced.

As described above, in relation to the first aspect, the trend in theresonance metric may be measured over a given number of the plurality ofparts. In this regard, the given number of parts may be according to anyof the values described above in relation to the first aspect.

Further still, the method of the fifth aspect may include performingvibrational testing of the parts such that the method may also includeexciting each of the plurality of parts at the plurality of inputfrequencies and measuring the frequency response of the each of theplurality of parts. The method may further include generating thevibrational data for each of the plurality of first parts based on themeasured frequency response of each respective one of the plurality offirst parts.

A sixth aspect of the disclosure may be characterized as a tool formonitoring a part production process using vibrational data regardingparts produced by the part production process. The tool includes a datastore comprising vibrational data for each of a plurality of partsproduced by the part production process. The vibrational data includes afrequency response of each of the plurality of parts when excited at aplurality of input frequencies. The tool also includes a vibrationevaluation module operative to access the data store to retrieve thevibrational data. The vibration evaluation module is operative togenerate a resonance metric from the vibrational data for each of theplurality of parts that is correlated to a process variable of the partproduction process, monitor the resonance metric over the plurality ofparts produced by the part production process to identify a trend in theresonance metric relative to the sequence of production of the pluralityof parts, and identify a change in the process variable associated withthe trend.

A number of feature refinements and additional features are applicableto the sixth aspect. These feature refinements and additional featuresmay be used individually or in any combination. As such, each of thefollowing features that will be discussed may be, but are not requiredto be, used with any other feature or combination of features of thesixth aspect.

For instance, the tool of the sixth aspect may be used to perform any orall of the features of the method of the fifth aspect. In this regard,any feature described above in relation to the method of the fifthaspect may be applicable to and/or performed by the tool of the sixthaspect.

Additionally, in an embodiment, of the tool of the sixth aspect, thetrend may be indicative of a state of the process variable. In turn, thetool may also include a control module that may be operative todetermine an adjustment to a control of the part production processrelated to the process variable in response to the state of the processvariable. The control module may be operative to adjust the control inresponse to the process variable.

In an embodiment, the process variable may include a manufacturingvariable. The manufacturing variable may include at least one of aprocess temperature, a process rate, manufacturing component wear, or araw material property. Additionally or alternatively, the processvariable may relate to a component variable. The component variable mayresult from the process variable. The component variable may include atleast one of a part dimension, a stress state, a crystallographicorientation, a material property, phase ratios, part chemistry, or partmicrostructure.

In an embodiment, the control module may adjust the control prior to theresonance metric exceeding a limit relative to the resonance metric. Thelimit may define at least one of a compliant part classification sortresult or a non-compliant part classification sort result. The limit mayinclude a bound on the resonance metric within a compliant partclassification sort result. In this regard, the bound may allow for thelimit to be exceeded to provide for control of the process variablewithout parts being categorized as non-compliant.

The trend in the resonance metric may be measured over a given number ofthe plurality of parts. In an embodiment, the given number of theplurality of parts may be at least 10 parts or according to thedefinition provided above in relation to the first aspect. Additionally,the given number of the plurality of parts may be based on a productionrate of the part production process. Alternatively, the trend in thereasoned metric may be measured over a given time period in which theplurality of parts are produced. The given time period may be at least0.5 hours or according to the definition provided above in relation tothe first aspect. The given time period may be based on a productionrate of the part production process.

The tool may also include a vibrational testing system operative toexcite each of the plurality of parts at the plurality of inputfrequencies and measuring the frequency response of the each of theplurality of parts. The vibrational testing system may be operative togenerate the vibrational data for each of the plurality of first partsbased on the measured frequency response of each respective one of theplurality of first parts.

A seventh aspect may be characterized as a method for use in vibrationaltesting of parts. The method includes acquiring vibrational data foreach of a plurality of parts produced by a part production process. Thevibrational data includes a frequency response of each of the pluralityof parts when excited at a plurality of input frequencies. The methodalso includes generating a plurality of resonance metrics from thevibrational data. The method includes obtaining process variable valuesfor a process variable for each of the plurality of parts andidentifying a correlation between the process variable and a correlatedresonance metric from the plurality of resonance metrics.

A number of feature refinements and additional features are applicableto the seventh aspect. These feature refinements and additional featuresmay be used individually or in any combination. As such, each of thefollowing features that will be discussed may be, but are not requiredto be, used with any other feature or combination of features of theseventh aspect.

For instance, as described above in relation to the determination of acorrelation between a response characteristic and a process variable inthe first aspect, identifying the correlation may include evaluationrelative to a multidimensional data set in which each of the pluralityof vibrational metrics comprise corresponding respective vibrationalmetric dimensions in the multidimensional data set and the processvariable comprises a non-vibrational dimension in the multidimensionaldata set. The identifying the correlation may also include evaluating aplurality of vibrational metric dimensions relative to thenon-vibrational dimension to determine the correlation between a givenresonance metric dimension and the non-vibrational dimension. Theevaluation may include at least one of a classification analysis or aregression analysis such as, for example, at least one of a non-linearleast squares regression, a correlation coefficient analysis, ananalysis of variance (ANOVA), k-means clustering, principal componentsanalysis, or random forest analysis.

Further still, the method of the seventh aspect may include vibrationaltesting of the parts. As such, the method may include exciting the firstpart at the plurality of input frequencies and measuring the frequencyresponse of the first part. The method may further include generatingthe vibrational data for the first part based on the measured frequencyresponse of the first part. The process variable of the seventh aspectmay be according to any of the process variables described above inrelation to the first aspect.

The method of the seventh aspect may also include controlling the partproduction process to vary the process variable such that at least twoof the plurality of parts have different process variable values. Thismay allow variation in the process variable such that a responsecharacteristic may be identified from the vibrational data.

Further still, the method of the seventh aspect may facilitateevaluation of a change in a default part production process.Accordingly, the method may include modifying the part productionprocess from a default part production process to a test part productionprocess to generate a plurality of test parts using the test partproduction process. The test part production process comprises a changein relation to at least the process variable. In turn, the method mayinclude monitoring the correlated resonance metric for each of theplurality of test parts in relation to a sort to assign each of theplurality of test parts to one of a compliant part classification or anon-compliant classification based on the sort. The method may includeevaluating the test part production process in relation to theassignment of the plurality of test parts to the compliant partclassification or the non-compliant part classification.

In addition to evaluation of individual ones of a plurality of testparts, the evaluation of the test part production process may involvebatch testing. As such, the method may also include comparing collectivevibrational data regarding the plurality of test parts that comprise atest batch relative to a batch sort that evaluates an entirety of thetest batch and determining whether the test batch satisfies a batchthreshold relative to the entirety of the test batch based on the batchsort. The batch threshold may relate to the correlated resonance metric.

An eighth aspect of the disclosure may be characterized as a tool foruse in identifying a correlation between vibrational data regarding atleast one part and a process variable used to manufacture the at leastone part. The tool includes a data store comprising vibrational data foreach of a plurality of parts produced by a part production process. Thevibrational data includes a frequency response of each of the pluralityof parts when excited at a plurality of input frequencies. The tool alsoincludes a correlation analysis module operative to generate a pluralityof resonance metrics from the vibrational data, obtaining processvariable values for a process variable for each of the plurality ofparts, and identify a correlation between the process variable and acorrelated resonance metric from the plurality of resonance metrics.

A number of feature refinements and additional features are applicableto the eighth aspect. These feature refinements and additional featuresmay be used individually or in any combination. As such, each of thefollowing features that will be discussed may be, but are not requiredto be, used with any other feature or combination of features of theeighth aspect.

For instance, the tool of the eighth aspect may be used to perform anyor all of the features of the method of the seventh aspect. In thisregard, any feature described above in relation to the method of theseventh aspect may be applicable to and/or performed by the tool of theeighth aspect.

Additionally, in relation to the operation of the correlation analysismodule, the correlation analysis module may be operative to prepare amultidimensional data set in which each of the plurality of vibrationalmetrics comprise corresponding respective vibrational metric dimensionsin the multidimensional data set and the process variable comprises anon-vibrational dimension in the multidimensional data set. Thecorrelation analysis module may evaluate a plurality of vibrationalmetric dimensions relative to the non-vibrational dimension to determinethe correlation between a given resonance metric dimension and thenon-vibrational dimension. In this regard, the correlation analysismodule may execute at least one of a classification analysis or aregression analysis. Specifically, the correlation analysis module mayexecute at least one of a non-linear least squares regression, acorrelation coefficient analysis, an analysis of variance (ANOVA),k-means clustering, principal components analysis, or random forestanalysis.

In an embodiment, the tool may include a vibrational testing systemoperative to excite the first part at the plurality of input frequenciesand measure the frequency response of the first part. The vibrationaltesting system may generate the vibrational data for the first partbased on the measured frequency response of the first part.

Additionally, in an embodiment, the process variable may include amanufacturing variable. The manufacturing variable may include at leastone of a process temperature, a process rate, manufacturing componentwear, or a raw material property. Additionally or alternatively, theprocess variable may relate to a component variable. The componentvariable may result from the process variable. The component variablemay include at least one of a part dimension, a stress state, acrystallographic orientation, a material property, phase ratios, partchemistry, or part microstructure.

Further still, the tool may include a control module that is operativeto control the part production process to vary the process variable suchthat at least two of the plurality of parts have different processvariable values. The control module may be operative to modify the partproduction process from a default part production process to a test partproduction process to generate a plurality of test parts using the testpart production process. The test part production process comprises achange in relation to at least the process variable. In turn, thevibrational testing system may be operative to monitor the correlatedresonance metric for each of the plurality of test parts in relation toa sort to assign each of the plurality of test parts to one of acompliant part classification or a non-compliant classification based onthe sort. The correlation analysis module may be operative to evaluatethe test part production process in relation to the assignment of theplurality of test parts to the compliant part classification or thenon-compliant part classification.

Additionally, the vibrational testing system may include a batchevaluation module that is operative to compare collective vibrationaldata regarding the plurality of test parts that comprise a test batchrelative to a batch sort that evaluates an entirety of the test batchand determine whether the test batch satisfies a batch thresholdrelative to the entirety of the test batch based on the batch sort. Thebatch threshold may relate to the correlated resonance metric.

A ninth aspect of the present disclosure may be characterized as amethod for evaluation of a change in a part production process thatincludes modifying a part production process from a default partproduction process to a test part production process to generate aplurality of test parts using the test part production process. The testpart production process includes a change in relation to at least oneprocess variable of the part production process. In turn, the methodincludes acquiring vibrational data regarding the plurality of testparts produced by the test part production process. The vibrational dataincludes a frequency response of the plurality of test parts whenexcited at a plurality of input frequencies. The method also includescomparing the vibrational data regarding the plurality of test partsproduced by the test part production process to qualificationvibrational data from a qualification population of parts and evaluatingthe test part production process based on the comparing.

A number of feature refinements and additional features are applicableto the ninth aspect. These feature refinements and additional featuresmay be used individually or in any combination. As such, each of thefollowing features that will be discussed may be, but are not requiredto be, used with any other feature or combination of features of theninth aspect.

For example, in an embodiment, the vibrational data may include thefrequency response of each one of the plurality of test parts. Themethod may also further include testing the vibrational data for eachone of the plurality of test parts against a sort. The sort may be basedupon the qualification vibrational data from the qualificationpopulation of parts. In turn, the method may include assigning each oneof the plurality of test parts to one of a compliant part classificationor a non-compliant classification based on the sort. Accordingly, theevaluating of the test part production method may be at least in partbased on the assigning. Further still, the evaluation of the test partproduction method may involve a batch evaluation such that thevibrational data comprises collective vibrational data regarding a testproduction batch comprising the plurality of test parts. In turn, themethod may include testing the collective vibrational data for the testpart batch against a batch sort that collectively evaluates an entiretyof the test production batch and determining whether the test productionbatch satisfies a batch threshold relative to the entirety of the testproduction batch based on the batch sort. As such, the evaluating may beat least in part based on the determining.

The test part production process may include a change to a processvariable of the part production process relative to the default partproduction process. The process variable may be according to thedescription of the process variable provided above in relation to thefirst aspect. The process variable may be correlated to a responsecharacteristic in the vibrational data. In turn, the comparing mayinclude comparing the response characteristic of the vibrational dataregarding the plurality of test parts to the response characteristic ofthe qualification population of parts. The vibrational data may includeresonance response data and the response characteristic corresponds to aresonance metric based on the resonance response data.

A tenth aspect may be characterized as a tool for evaluation of a changein a part production process. The tool includes a control module that isoperative to modify a part production process from a default partproduction process to a test part production process to generate aplurality of test parts using the test part production process. The testpart production process comprises a change in relation to at least oneprocess variable of the part production process. The tool also includesa data store comprising vibrational data regarding the plurality of testparts produced by the test part production process. The vibrational dataincludes a frequency response of the plurality of test parts whenexcited at a plurality of input frequencies. The tool also includes avibrational testing system operative to compare the vibrational dataregarding the plurality of test parts produced by the test partproduction process to qualification vibrational data from aqualification population of parts and evaluate the test part productionprocess based on the comparing.

A number of feature refinements and additional features are applicableto the tenth aspect. These feature refinements and additional featuresmay be used individually or in any combination. As such, each of thefollowing features that will be discussed may be, but are not requiredto be, used with any other feature or combination of features of thetenth aspect.

For instance, the tool of the tenth aspect may be used to perform any orall of the features of the method of the ninth aspect. In this regard,any feature described above in relation to the method of the ninthaspect may be applicable to and/or performed by the tool of the tenthaspect.

Additionally, the vibrational data includes the frequency response ofeach one of the plurality of test parts. The vibrational testing systemmay be operative to test the vibrational data for each one of theplurality of test parts against a sort and assign each one of theplurality of test parts to one of a compliant part classification or anon-compliant classification based on the sort. The sort may be basedupon the qualification vibrational data from the qualificationpopulation of parts. In turn, the vibrational testing system mayevaluate the test part production process at least in part based on theassigning.

In an embodiment, the vibrational data may include or define collectivevibrational data regarding a test production batch comprising theplurality of test parts. In turn, the tool may further include a batchevaluation module operative to test the collective vibrational data forthe test part batch against a batch sort that collectively evaluates anentirety of the test production batch and determine whether the testproduction batch satisfies a batch threshold relative to the entirety ofthe test production batch based on the batch sort. The vibrationaltesting system may be operative to evaluate the test part productionprocess at least in part based on the batch evaluation moduledetermining whether the test production batch satisfies a batchthreshold relative to the entirety of the test production batch based onthe batch sort.

In an embodiment, the control module may change a process variable ofthe part production process relative to the default part productionprocess to perform the test part production process. The processvariable may be correlated to a response characteristic in thevibrational data, and the vibrational testing system may compare theresponse characteristic of the vibrational data regarding the pluralityof test parts to the response characteristic of the qualificationpopulation of parts. The vibrational data may include resonance responsedata and the response characteristic corresponds to a resonance metricbased on the resonance response data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of an embodiment of a process control systemthat may be useful for generation of process feedback data based on avibrational analysis of parts produced by a part production system.

FIG. 2 is a block-diagram of one embodiment of a vibrational testingsystem.

FIG. 3 shows a simplified block diagram of the vibrational testingsystem of FIG. 1.

FIG. 4 is a block-diagram of another embodiment of a vibrational testingsystem.

FIG. 5 presents various resonance inspection results of parts that mayillustrate operation of a vibrational testing system.

FIG. 6 is a plot of vibrational data for a plurality of parts arrangedinto batches that may be used for batch evaluation of parts.

FIG. 7 is a plot of vibrational data for a plurality of parts that maybe used to identify a trend in the vibrational data.

FIG. 8 is flowchart depicting an embodiment of a method related todetermining a correlation between a process variable and a vibrationalmetric.

FIG. 9 is a flowchart depicting an embodiment of a method for evaluationof a test part production process in relation to a correlated responsecharacteristic in resulting test parts produced by the test partproduction process.

FIGS. 10 and 11 are flowcharts depicting embodiments of methods forevaluation and process control feedback generation.

DETAILED DESCRIPTION

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that it is not intended to limit the inventionto the particular form disclosed, but rather, the invention is to coverall modifications, equivalents, and alternatives falling within thescope of the invention as defined by the claims.

FIG. 1 depicts an embodiment of a process control system 1. The processcontrol system 1 may include a part production system 2. As will bedescribed in greater detail below, the part production system 2 mayperform a part production process to produce one or more parts 4. As maybe appreciated, the part production system 2 may be used to produce aplurality of parts 4. As used herein, a single part 4 or a plurality ofparts 4 may be referenced, all of which may be produced by a partproduction process of the part production system 2 unless otherwisespecified, although production of various parts 4 may occur at differenttimes using different process variables of the part production process.

The process control system 1 may also include a vibrational testingsystem 6 that may test the one or more parts 4 and may provide feedbackto the part production system 2. As further depicted in FIG. 1, theprocess control system 1 may include a correlation analysis module 8that may be in operative communication with the vibrational testingsystem 6 to obtain frequency responses determined by the vibrationaltesting system 6 corresponding to tested parts 4. The correlationanalysis module 8 may also be in operative communication with the partproduction system 2 to obtain process variable information regarding theproduction of parts 4. In turn, the correlation analysis module 8 may beoperative to determine correlations between the frequency responses ofparts 4 and the part production process carried out by the partproduction system 2. Various embodiments are described below that mayrelate to features and/or performance of individual ones of thecomponents of the process control system 1. Furthermore, the discussionbelow describes features and/or performance of components of the processcontrol system 1 collectively. In this regard, the discussion thatfollows is illustrative of embodiments of a process control system 1 andthe individual components thereof. As will be appreciated in thefollowing discussion, various embodiments of a tool for use in providingprocess control feedback may include one or more components of thesystem.

Specifically, the vibrational testing system 6 may be operative toexcite a part 4 to collect a vibrational response thereof as vibrationaldata regarding the part 4. The vibrational testing of the part 4 maycomprise a resonance inspection of the part 4. Various applications ofresonance inspection (e.g., resonance ultrasound spectroscopy; processcompensated resonance testing) are addressed herein. Various principlesthat may relate to resonance inspection are addressed in the followingU.S. patents, the entire disclosures of which are incorporated byreference in their entirety herein: U.S. Pat. Nos. 5,408,880; 5,425,272;5,495,763; 5,631,423; 5,641,905; 5,837,896; 5,866,263; 5,952,576;5,965,817; 5,992,234; and 6,199,431.

One embodiment of a resonance inspection tool or system 5 (e.g., foraccommodating resonant ultrasound spectroscopy measurement with aplurality of sensors; for process compensated resonance testing) isillustrated in FIGS. 2 and 3. As may be appreciated, the resonanceinspection tool 5 may comprise the vibrational testing system 6 of FIG.1, although in other embodiments of the process control system 1 anyappropriate vibrational testing system 6 may be used without limitation.The resonance inspection tool 5 includes a computer 10 that provides forcontrol of a synthesizer 12 and an analog to digital converter 11 foreach data input channel connected to each receiving or responsetransducer 22, 24 of the resonance inspection tool 5. Transducer 22 hasan output on line 31, while transducer 24 has an output on line 25.

Synthesizer 12 may have a frequency range from greater than 0 to 20 MHz.Other frequency ranges may be appropriate. Synthesizer 12 provides twooutputs which are the frequency F1 at output 14 and a second outputwhich is the frequency F2 at line 16. In one embodiment, the frequencyF2 is either F1 plus a constant frequency such as 1000 Hz for heterodyneoperation of the receiver, or at F1 for homodyne operation. A firsttransducer 18 (e.g., the input or driving transducer) is excited at afrequency F1 by synthesizer 12. Transducer 18 provides vibration (e.g.,ultrasonic) to an object 20 to be tested via resonance inspection.

The response of the object 20 is then received by two separate outputtransducers 22 and 24. The circuitry from the output transducer 22 andA/D converter 11 can be identical to circuitry between output transducer24 and A/D converter 11. For this reason, only the circuitry betweenoutput transducer 22 and A/D converter 11 will be discussed below. Thetimes one (×1) amplifier 26 is connected to the output transducer 22,provides current for transformer 28, and has a feedback 27.

The output of transducer 22 is connected to a receiver 41 (FIG. 3).Receiver 41 is used for the purpose of providing amplification and noiserejection in the circuit between output transducer 22 and A/D converter11. The output A (line 40) is applied to the A/D converter 11 within thecomputer 10. The A/D converter 11 provides an A/D conversion for each oflines 40 and 42. The converted information is then entered into a filewhich consists of the measured frequency, the amplitude of A, theamplitude of B, the amplitude of A plus B, and the amplitude of A minusB. This file is then used for further analysis of the spectrum todetermine characteristics of a part 20 being tested. The file may bestored in a data store comprising a physical memory device 121 (FIG. 4).In other embodiments, the file stored in the data store may include anyappropriate vibrational data obtained from vibrational testing (e.g.,using the vibrational testing system 6 including raw vibrational dataand/or vibrational data metrics obtained from the raw vibrational data.

The times one (×1) amplifier 26 provides feedback to an inner coaxialcable shield 30 which surround the lead from transducer 22 to amplifier26. Shield 30 is another grounded shield which can also be used fornoise suppression. The outer surrounding coaxial cable is not shown inFIG. 2. If lead 31 is short, the shield 30 may be omitted becausecapacitance will not be too large. The purpose of the inner shield 30 isto provide a cancellation of capacitance of the lead 31. The transformer28 may be a 4:1 step-down transformer used for impedance matching to theinput of amplifier 32. In this regard, it should be noted that theoutput impedance of amplifier 26 may be much lower than the outputimpedance of transducer 22. This provides for the power gain and thenecessary feedback to shield 30. The amplifier 32 may have a gain factorof 100:1 or a 40 db gain. Other gain factors may be appropriate. Theamplifier 26 may be a broad-band amplifier having a band pass on theorder of 50 MHz.

Mixer 34 has an output signal (e.g., a 1 KHz signal) having a magnitudewhich is proportional to the magnitude of the frequency F1 provided online 14 from synthesizer 12. The function of the synthesizer 12 is toprovide a point-by-point multiplication of instantaneous values ofinputs on lines 16 and 33. The mixer 34 also has many high frequencyoutput components which are of no interest. The high frequencycomponents are therefore filtered out by the low-band pass filter 38which is connected to mixer 34 by line 36. Filter 38 serves to clean-upthe signal from mixer 34 and provide a voltage on line 40 which is onlythe output signal at an amplitude which is proportional to the amplitudeof the output 31 of transducer 22.

Operation of the resonance inspection tool 5 will be briefly describedin relation to measurement steps performed by measurement of the outputof either transducer 22 or transducer 24 controlled by computer 10. Ameasurement cycle may be initiated, and provides initialization for thefrequency F and the desired frequency step. The frequency step may be 1Hz or any other frequency selected for the measurement. Although aconstant frequency step may be utilized, the frequency step may bedetermined by any appropriate algorithm. In one embodiment, thefrequency step is determined by determining the start frequency and thestop frequency, and dividing the frequency difference by the number ofsteps desired for the measurement. In any case, the synthesizer 12 isconfigured to provide a plurality of input or drive frequencies totransducer 18.

Once a signal is picked up by the receiver (i.e., an output on line 33),a pause for ring delay there is a provided. The pause for ring delay maybe on the order of 30 milliseconds, although other ring delays can beused if the object under test 20 has resonances that are narrower than afew hertz. The purpose of the pause is to give the object 20 anopportunity to reach its steady state magnitude in response to a steadyinput from transducer 18. The pause time is time after the frequency isapplied and before detection is initiated.

After the ring delay is complete, analog-to-digital converter 11provides an output that can be used by the data recording computer. Theoutput of the A/D conversion is then written to a file by the computer10 for the purpose of analysis of the data by another program or storagein the data store of the physical memory device 121. This data may bereferred to herein as vibrational data. Vibrational data comprising theunique signature or characterizing of the object 20 is written into fileas it is created. Reading may be stopped when a read frequency ispresent and step 66 stops the program. Once information is entered intofile, subsequent processing can be used to generate one or morevibrational metrics that may, for example, include a signature orcharacterize the object 20. Examples of vibrational metrics (which mayinclude, but are not limited to, metrics related to the resonanceresponse of the object 2A or “personal metrics”) may include, but arenot limited to, vibration (resonant) magnitudes, the sum of resonantmagnitudes, the difference of vibration (resonant) magnitudes, or othermanipulations of the multiple channel multiple frequency measurementwhich is used to perform the unique signature of the object 20. Themagnitude of the outputs at each sensor location for each resonancefrequency may be compared.

Another embodiment of a resonance inspection tool or system isillustrated in FIG. 4 and is identified by reference numeral 100. In anembodiment, the resonance inspection tool 100 may comprise thevibrational testing system 6 of FIG. 1. The resonance inspection tool100 may be used to assess a part or part-under-test 120. This part 120may be retained in a fixture 119 in any appropriate manner for executionof a resonance inspection.

The resonance inspection tool 100 includes a signal generator 102 of anyappropriate type, at least one transducer (e.g., transducer 104), and acomputer 108. The transducer 104 may be of any appropriate type. In oneembodiment, the transducer 104 is in physical contact with the part 120throughout execution of the inspection of the part 120, and in thiscase, may be characterized as being part of the fixture 119 for the part120. Another embodiment has the transducer 104 being maintained inspaced relation to the part 120 throughout execution of the resonanceinspection of the part 120 (e.g., a laser, such as Nd:YAG lasers, TEACO2 lasers, excimer lasers, or diode lasers).

The computer 108 may include what may be characterized as a vibrationalor resonance assessment module 110 (e.g., incorporated/embodied by anon-transitory computer-readable storage medium). Generally, theresonance assessment module 110 may be configured to evaluate theresults of a resonance inspection, for instance for purposes ofdetermining whether the part 120 should be accepted or rejected by theresonance inspection tool 100, determining whether the part 120 is at anend-of-life state or condition, or the like. A part 120 that is“accepted” by the resonance inspection tool 100 may mean that theresonance inspection tool 100 has determined that the part 120 may beput into service (e.g., utilized for its intended purpose(s) and/or usedaccording to its design specifications). In one embodiment, a part 120that has been accepted by the resonance inspection tool 100 means thatthe tool 100 has determined that the part 120 is free of defects, is notin an end-of-life condition or state, is aging normally, or anycombination thereof. A part 120 that is “rejected” by the resonanceinspection tool 100 may mean that the resonance inspection tool 100 hasdetermined that the part 120 should not be put into service (e.g.,should not be utilized for its intended purpose(s) and/or should nolonger be used according to its design specifications). In oneembodiment, a part 120 that has been rejected by the resonanceinspection tool 100 means that the tool 100 has determined that the part120 includes at least one defect, is at or near an end-of-life conditionor state, is aging abnormally, or any combination thereof. A part 120that is analyzed or assessed by the resonance inspection tool 100 may beof any appropriate size, shape, configuration, type, and/or class. Forexample, the part 120 may comprise a new production part—a newlymanufactured part that have not yet been released from production (e.g.,a part that have not been shipped for use by an end user or customer).New production parts include parts that may have undergone at least somepost-production testing of any appropriate type (including withoutlimitation a resonance inspection).

The signal generator 102 generates signals that are directed to thetransducer 104 for transmission to the part 120 in any appropriatemanner/fashion (e.g., via physical contact between the transducer 104and the part 120; through a space between the transducer 104 and thepart 120). Signals provided to the transducer 104 by the signalgenerator 102 are used to mechanically excite the part 120 (e.g., toprovide energy to the part 120 for purposes of inducing vibration).Multiple frequencies may be input to the part 120 through the transducer104 in any appropriate manner. This may be characterized as “sweeping”through a range of frequencies that are each input to the part 120, andthis may be done in any appropriate manner for purposes of the resonanceinspection tool 100. Any appropriate number/range of frequencies may beutilized, and any appropriate way of progressing through a plurality offrequencies (e.g., a frequency range) may be utilized by the resonanceinspection tool 100.

In one embodiment, at least one other transducer 106 is utilized in theresonance inspection of the part 120 using the resonance inspection tool100 of FIG. 4, including where two transducers 106 are utilized (e.g.,in accordance with the embodiment of FIGS. 2 and 3 noted above). Each ofthe transducers 106, as well as the input or drive transducer 104, maybe in physical contact with the part 120. It may be such that the part120 is in fact entirely supported by the transducer 104 and anyadditional transducers 106 (e.g., the drive transducer 104 and one ormore receive transducers 106 may define the fixture 119). Eachtransducer 106 that is utilized by the resonance inspection tool 100 isused to acquire the frequency response of the part 120 to thefrequencies input to the part 120 by the drive transducer 104, andtherefore each transducer 106 may be characterized as an output orreceiver transducer 106. The frequency response may be measured asvibrational data that is descriptive of the frequency response of thepart 120.

One or more transducers 106 utilized by the resonance inspection tool100 may be maintained in physical contact with the part 120 throughoutthe resonance inspection. Another option is for one or more of thetransducers 106 to be maintained in spaced relation with the part 120throughout the resonance inspection. A transducer 106 in the form of alaser may be maintained in spaced relation with the part throughout theresonance inspection, and may be utilized to obtain the frequencyresponse of the part 120 to generate vibrational data descriptive of thefrequency response. Representative lasers that may be utilized as atransducer 106 by the resonance inspection system 100 include withoutlimitation Nd:YAG lasers, TEA CO2 lasers, excimer lasers, or diodelasers. In one embodiment, the frequency response of the part 120 isacquired by laser vibrometry utilizing at least one transducer 106. Agiven transducer 106 in the form of a laser may acquire resonance dataon the part 120 from a single location, or a given transducer 106 in theform of a laser could acquire resonance data on the part 120 by scanningthe laser over multiple locations on the part 120.

Another embodiment of the resonance inspection tool 100 of FIG. 4utilizes only the transducer 104. That is, no additional transducers 106are utilized by the resonance inspection tool 100 in this case, andtherefore the transducer 106 is presented by dashed lines in FIG. 4. Inthis case, the transducer 104 is used to input a drive signal to thepart 120 (e.g., to excite the part 120 at a plurality of differentfrequencies), and is also used to acquire the frequency response of thepart 120 to these input drive frequencies to generate the vibrationaldata for the part 120 that describes the frequency response of the part120. Representative configurations for this drive/receive transducerconfiguration 104 include without limitation piezoceramic,piezocomposites, piezoelectric quartz crystal, and otherelectromechanical materials.

In the above-noted drive/receive transducer configuration 106, a firstdrive signal at a first frequency (from the signal generator 102) may betransmitted to the part 120 through the transducer 104, the transmissionof this first drive signal may be terminated, and the transducer 104 maybe used to acquire a first frequency response of the part 120 to thisfirst drive signal (including while a drive signal is being transmittedto the part 120). The signal generator 102 may also be used provide asecond drive signal at a second frequency to the transducer 104, whichin turn transmits the second drive signal to the part 120, thetransmission of this second drive signal may be terminated, and thetransducer 104 may once again be used to acquire a second frequencyresponse of the part 120 to this second drive signal (including while adrive signal is being transmitted to the part 120). This may be repeatedany appropriate number of times and utilizing any appropriate number offrequencies and frequency values. In this regard, the first frequencyresponse, the second frequency response, and any further frequencyresponse may comprise the vibrational data for the part. One or moredrive signals may be sequentially transmitted to the part 120 by thesignal generator 102 and transducer 104, one or more drive signals maybe simultaneously transmitted to the part 120 by the signal generator102 and transducer 104, or any combination thereof.

The frequency response of the part 120 is transmitted to the computer108 of the resonance inspection tool 100 of FIG. 4 to generate and/orstore the vibrational data regarding the part 120. This computer 108 maybe of any appropriate type and/or configuration, and is used by theresonance inspection tool 100 to evaluate the part 120 in at least somefashion (e.g., to determine whether to accept or reject the part 120).Generally, the part 120 is vibrated by the transducer 104 according to apredetermined signal(s), and the part 120 is evaluated by the resultingvibrational (e.g., whole body) response of the part 120. For instance,this evaluation may entail assessing the part 120 for one or moredefects of various types, assessing whether the part 120 is at or nearthe end of its useful, life, assessing whether the part 120 is agingnormally or abnormally, or any combination thereof. Further still, theevaluation may entail assessing the part production process used toproduce the part as will be described in greater detail below. In anyrecord, the vibrational data may be stored in a data store comprising aphysical memory device 121.

The computer 108 may incorporate and utilize the above-noted resonanceassessment module 110 to evaluate the response of the part 120 to aresonance inspection. The resonance assessment module 110 may be of anyappropriate configuration and may be implemented in any appropriatemanner. In one embodiment, the resonance assessment module 110 includesat least one part sort logic 112 (e.g., logic configured to determinewhether to accept or reject parts) along with one or more processors 116of any appropriate type and which may be implemented in any appropriateprocessing architecture. The assessment of the response of the part 120to the input drive signals may entail comparing the response to alibrary 118 utilized by the resonance inspection tool 100. This library118 may be stored on a computer-readable storage medium 121 of anyappropriate type or types and in a non-transitory form (e.g., anon-transitory computer-readable storage medium), including withoutlimitation by using one or more data storage devices of any appropriatetype and utilizing any appropriate data storage architecture. As may beappreciated, both the vibrational data for the part under test and thecomparative resonance data may both be stored in the library 118 that isaccessible by the resonance inspection tool 100. While one physicalstorage device 114 is shown, additional physical storage devices may beprovided without limitation.

The library 118 of the resonance inspection tool 100 may include varioustypes of resonance inspection results to allow the resonance inspectiontool 100 to assess a part 120. Generally, the resonance inspectionresults from the part 120 are compared with comparative resonance datain the library 118 from at least one other part that is the same as thepart 120 in one or more respects (e.g., a part 120 in the form of aturbine blade will be compared to turbine blade data in the library 118;a part 120 in the form of a turbine blade will not be compared with ballbearing data in the library 118). The library 118 may includevibrational data from a qualification population of parts that areclassified as acceptable or compliant. In this regard, a sort toevaluate a part 120 may include evaluating the vibrational data for thepart 120 to the vibrational data of the qualification population ofparts. For instance, representative resonance inspection results arepresented in FIG. 5, and are of a type that may be included in thelibrary 118. The three spectra 122 shown in FIG. 5 may represent thefrequency response of a part 120 or collection of qualification parts120 that have been determined to be acceptable. This may define aclassification of compliant parts. Note how the three peaks 128 a, 128b, and 128 c differ in at least one respect between the various spectra122.

The three spectra 124 shown in FIG. 5 may represent the frequencyresponse of a part 120 to a certain input frequency, and where thisin-service part 120 has been accepted by the resonance inspection tool100. Note how the three peaks 128 a, 128 b, and 128 c in the spectra 124may differ slightly yet generally conform with the corresponding peaks128 a, 128 b, and 128 c in the spectra 122 (again, associated with a newproduction part 120).

The three spectra 126 shown in FIG. 5 represent the frequency responseof a part 120 to a certain input frequency, and where the part 120 hasbeen rejected by the resonance inspection tool 100. Note how the threepeaks 128 a, 128 b, and 128 c in the spectra 126 differ in at least onerespect from the corresponding peaks 128 a, 128 b, and 128 c in thespectra 122 (again, associated with a part 120 that the resonanceinspection tool 100 would accept or classify as compliant). Generally,each of the peaks 128 a, 128 b, and 128 c in the spectra 126 has shiftedto the left compared to the corresponding peaks 128 a, 128 b, and 128 cin the spectra 122 and 124. Moreover, note the “compression” between thepeaks 128 a, 128 b in the spectra 126 compared to the spectra 122, 124,as well as the “compression” between the peaks 128 b, 128 c in thespectra 126 compared to the spectra 122, 124.

In this regard, evaluation or testing of a part against a sort mayresult in the part being classified into at least one of a compliantpart classification or a non-compliant part classification based on acomparison of the vibrational data for a part 120 to vibrational datafor a qualification part population. In this regard, the vibrationaldata regarding the qualification part population may be a statisticalrepresentation of vibrational data regarding a plurality of parts thatcomprise the qualification part population. The vibrational data for thequalification part population may be gathered in the same mannerdescribe above in relation to a part 120. For instance, thequalification part population may comprise vibrational data for parts120 that undergo subsequent evaluation to determine the parts definecompliant parts (e.g., by subsequent destructive testing to validate theparts as compliant).

The vibrational data regarding the qualification part population mayinclude or be used to generate one or more metrics (e.g., resonancemetrics) as described above. In turn, the sort may include sortparameters defined relative to the vibrational data (e.g., including oneor more metrics) of the qualification part population that thevibrational data of a part under test is compared to during the sort.Such sort parameters may include, for example, minimum values, maximumvalues, acceptable ranges of values, unacceptable ranges of values,acceptable relative values, unacceptable relative values, or any otherappropriate measure or relative measure regarding the vibrational datathat may be used to evaluate whether a part under test is to beclassified into a compliant part classification or a non-compliant partclassification. As will be described in greater detail below, a sortparameter may include a bound (e.g., an upper boundary, a lowerboundary, or any other appropriate boundary) such that vibrational thatfalls outside the bound may result in a corresponding part beingclassified into a non-compliant part classification.

In this regard, the qualification part population may either correspondto a compliant part classification or a non-compliant partclassification. For instance, if the qualification part populationcorresponds to a compliant part classification, the evaluation of thevibrational data for a part under test may include comparing thevibrational data of the part under test to the sort parameters of thequalification part population. If the vibrational data of the part undertest satisfies the sort parameters of the qualification part population,the part under test may be classified into a compliant partclassification. Additionally or alternatively, in such a case, if thevibrational data of the part under test fails to satisfy the sortparameters of the qualification part population, the part under test maybe classified into a non-compliant part classification. Alternatively,the qualification part population may correspond to a non-compliant partclassification. If the vibrational data of the part under test satisfiesthe sort parameters of the qualification part population, the part undertest may be classified into a non-compliant part classification.Additionally or alternatively, in such a case, if the vibrational dataof the part under test fails to satisfy the sort parameters of thequalification part population, the part under test may be classifiedinto a compliant part classification.

With returned reference to FIG. 1, it may be appreciated that the partproduction system 2 may perform a batch part production process or acontinuous part production process. In the case of a batch partproduction process, parts 4 may be produced in batches such that aplurality of parts 4 that are each collectively produced in a batch ofthe batch part production process may define a batch of parts forpurposes of discussion herein. In the case of a continuous partproduction process, a batch of parts may be defined in relation to agiven number of parts produced or a given time period. For instance, fora continuous production process, a batch of parts may be defined as agiven number of a plurality of parts relative to the order in which theparts are produced. As an example, if the given number of parts is 50, abatch of parts may be defined for each successive group of 50 partsproduced by the part production process. In this context, the givennumber of the plurality of parts that define a batch may be based on aproduction rate of a part production process. For instance, a partproduction process with a relatively high rate of production may have arelatively large number of parts belonging to each batch such that thegiven number of the plurality of parts in a batch may be at least about100, at least about 250, at least about 500, or at least about 1,000. Insuch contexts, the given number of the plurality of parts in a batch maybe not more than about 5,000, not more than about 2,500, not more than2,000, or not more than about 1,000. For a part production process witha relatively low rate of production, a relatively small number of partsmay belong to each batch. For instance, the given number of theplurality of parts in a batch may be at least about 5, at least about10, at least about 25, or at least about 50. In such contexts, the givennumber of the plurality of parts in a batch may be not more than about100, not more than about 75, not more than 50, or not more than about25. In this regard, the given number of the plurality of parts thatbelong to a batch may be dependent on the production rate of the processsuch that for a given part production system 2 or a part productionprocess, the size of the batch may change based on the rate ofproduction actually realized at the time of producing the parts. Inaddition, the foregoing minimum and maximum examples of the given numberof the plurality of parts in a batch may define global maximums,minimums, or combinations thereof may define global ranges that may beapplied to any part production process, including for a given number ofparts over which an analysis of vibrational data for parts occurs.

Alternatively, for a continuous production process, a batch of parts maybe defined as parts produced by the process in a given period of time.As an example, if the given period of time is 1 hour, a batch of partsmay be defined as comprising each part produced by the part productionprocess during the 1 hour time period. In this context, the given timeperiod for production of the plurality of parts that define a batch maybe based on a production rate of a part production process. Forinstance, a part production process with a relatively high rate ofproduction may have a relatively small time period defining the partsbelonging to each batch such that the given time period defining theplurality of parts in a batch may be not more than about 10 minutes(0.167 hours), not more than about 15 minutes (0.25 hours), not morethan about 30 minutes (0.5 hours), not more than about 45 minutes (0.75hours), or not more than about 1 hour. In such contexts, the given timeperiod defining the plurality of parts in a batch may be at least about1 minute (0.017 hours), at least about 5 minutes (0.083 hours), at leastabout 10 minutes (0.167 hours), or at least about 15 minutes (0.25hours). For a part production process with a relatively low rate ofproduction, a relatively larger time period may define the number ofparts that belong to each batch. For instance, the given time perioddefining the plurality of parts in a batch may be at least about 15minutes (0.25 hours), at least about 30 minutes (0.5 hours), at leastabout 1 hour, or at least about 2 hours. In such contexts, the givennumber of the plurality of parts in a batch may be not more than about 8hours, not more than about 5 hours, not more than 3 hours, or not morethan about 1 hour. In this regard, as the given number of parts or thegiven time period defining the plurality of parts that belong to a batchmay be dependent on the production rate of the process such that for agiven part production system 2 or a part production process, the size ofthe batch may change based on the rate of production actually realizedat the time of producing the parts. In addition, the foregoing minimumand maximum examples may define global maximums, minimums, orcombinations thereof may define global ranges that may be applied to anypart production process. In any regard, vibrational testing to producevibrational data may allow for evaluation of parts 4 produced by a partproduction process relative to a batch of parts. Such collectiveevaluation of parts comprising a batch of parts may be referred to asbatch evaluation and may be performed by a batch evaluation module 130of the vibrational testing system 6. As a first example of potentialbatch evaluation, a batch of parts may be evaluated against a batchsort. In an embodiment, the batch sort may comprise a batch sortthreshold that, in a manner analogous to the sort parameters of a sortdescribed above, may be used for evaluation of a batch of parts.However, unlike the sort described above, the vibrational data evaluatedrelative to the batch threshold may comprise collective vibrational dataof the plurality of parts comprising the batch. In this regard, thecollective vibrational data may comprise a statistical representation ofthe vibrational data for the individual ones of the plurality of partswithin the batch. For instance, the collective vibrational data maycomprise a mean, median, standard deviation, range, or other statisticalrepresentation of any of the vibrational data of individual ones of theplurality of parts in the batch including metrics included in orgenerated from the vibrational data, including for a given time periodover which an analysis of vibrational data for parts occurs.

While the collective vibrational data of a batch may be based onvibrational data of the individual ones of the plurality of partscomprising the batch, the vibrational data of the individual one of theplurality of parts comprising the batch may be treated differently whengenerating the collective vibrational data. For instance, evaluation ofthe batch of parts may be skewed or otherwise effected by consideringoutliers among the individual parts in the batch. As such, thegeneration of the collective vibrational data may include discountingvibrational data for certain ones of the plurality of parts in thebatch. Such discounting may decrease the effect of vibrational data ofan individual part relative to the vibrational data of other parts ofthe batch of parts. It may be appreciated that vibrational data for atleast one of the individual parts may be, but is not required to be,entirely discounted so as not to effect or inform the collectivevibrational data of the batch of parts. For instance, vibrational dataof the individual parts may be discounted in the event that thevibrational data for the individual part falls outside a standarddeviation of the collective vibrational data, corresponds to a part in anon-compliant classification, or meets any other appropriate condition.

The evaluation of a batch of parts against a batch sort may result inany one or more of a number of different results relative to the batchof parts. For instance, the batch sort may be used to classify theentirety of the batch of parts into one of a compliant classification ora non-compliant classification (e.g., based on whether the collectivevibrational data satisfies the batch threshold). Alternatively, in theinstance where a batch of parts fails to meet the batch threshold of thebatch sort, the plurality of parts in the batch of parts may undergoadditional testing (e.g., to further evaluate the parts fordetermination of classification of the parts or for evaluation of aprocess used to produce the batch of parts). For instance, if the batchof parts fails to meet the batch threshold, each one of the individualones of the batch of parts may be further tested.

With further reference to FIG. 6, a plot 200 of vibrational data 210from a plurality of parts is shown. In the plot 200, vibrational data210 for each of the plurality of parts is plotted as a point. That is,each point represents the vibrational data (e.g., potentially includinga value for a vibrational metric) for a given part. The parts aregrouped by batch such that different adjacent batches in the plot 200are represented by a different plot point style (e.g., dot, cross, star,etc.). As may be appreciated in the plot 200, batch 212, batch 214, andbatch 216 are highlighted. These batches may comprise a population ofqualification parts. Such qualification parts and the resulting batchescorresponding thereto may provide for qualification data to establishthe batch threshold. For instance, one example of a batch threshold mayrelate to the total variation 218 of the collective vibrational data 210for a given batch. In turn, each batch of parts may be evaluatedrelative to the batch threshold for determining whether each given batchsatisfies the batch threshold for a given batch sort. In the exampleprovided in FIG. 6, batch 220 may have a total variation 222 thatexceeds the total variation 218 corresponding to the batch threshold. Inthis regard, batch 220 may be determined not to satisfy the batchthreshold and may be identified as failing the batch sort. In turn, asdescribed above, batch 220 may be flagged for further testing, theplurality of parts comprising batch 220 may be assigned to anon-compliant part classification, or some other appropriate action maybe taken relative to the batch 220. In the case where the plurality ofparts comprising the batch 220 are classified to a non-compliant partclassification, it may be appreciated that this may includereclassification of parts that may have been previously categorized intoa compliant part classification based on a part sort relative to theindividual vibrational data for the individual parts. In this regard, apart sort and a batch sort may be independently performed such that evenparts that satisfy a part sort may be reclassified or subjected tofurther testing as a result of belonging to a batch 220 that fails tosatisfy a batch threshold for a batch sort.

While the total variation 218 of the collective vibrational data 210 fora given batch is describes as corresponding to the batch threshold, itmay be appreciated that other batch thresholds may be defined forapplication in relation to a batch sort. This may include a change inthe collective vibrational data between a plurality of batches. Forinstance, if the change in the collective vibrational data between afirst and a second batch exceeds some predefined value, the second batchmay be determined to not satisfy the batch threshold for a batch sort.In other words, the batch threshold may comprise a maximum change incollective vibrational data between a plurality of batches (e.g.,including either adjacent batches or some number of batches over time)so that if the change in the vibrational data between the plurality ofbatches exceeds the maximum change, the batch that exceeds the maximumchange may not satisfy the batch threshold for the batch sort. As usedherein, the change in the collective vibrational data may comprise achange in a statistical representation of the collective vibrationaldata such as a vibrational data mean, a standard deviation, a median, orany other appropriate representation. As will be described in greaterdetail below, the batch sort may include identification of a trend inthe collective vibrational data, such that a trend that exceeds a limitmay not satisfy the batch threshold and thus, fail the batch sort.

With returned reference to FIG. 1, the vibrational testing system 6 mayprovide feedback to the part production system 2. In this regard, it maybe appreciated that a part production process may include processvariables that effect parts 4 produced by the part production process.Specifically, it has been found that the effects of such processvariables on parts 4 produced by the part production process maymanifest in the vibrational data regarding a part 4 produced by the partproduction process. A change in a process variable may produce arepeatable, observable result in the vibrational data regarding a part4. Accordingly and as will be described in greater detail below, acorrelation between the result in the vibrational data and a processvariable may be determined.

Specifically, a response characteristic may be identified thatcorrelates to the process variable of the part production process. Theresponse characteristic may provide information regarding a state of theprocess variable. In turn, a control for the process variable may beadjusted based on the identified state of the process variable from theresponse characteristic. By way of example, one such process variablemay be a temperature at which a process step occurs in the partproduction process. The temperature (i.e., the process variable) may becorrelated to a given response characteristics in the vibrational dataof parts 4 produced using the part production process. Specifically, theresponse characteristic may provide information regarding a state of theprocess variable such as, in this example, whether the temperature istoo high or too low and/or a magnitude of a deviation of the processvariable. In turn, a control (e.g., a thermostat, other device, or othercondition that effects the temperature of the part production process)may be adjusted in view of the response characteristics that isidentified from the vibrational data of parts 4 that are tested. Thevibrational testing system 6 may include a control module 148 that maydetermine an adjustment to a control of the part production systemand/or directs interface with the part production system 2 to adjust thecontrol.

Accordingly, the response characteristic, when identified from thevibrational data, may indicate that a process variable for a partproduction process is out of control or requires modification. In turn,a control for the process variable may be adjusted based on theidentification of the response characteristic. Specifically, theresponse characteristic may provide information on the state of theprocess variable. This information regarding the state of the processvariable may include information regarding a direction and/or magnitudeof adjustment needed for the control of the process variable. Forinstance, continuing the example from above, the response characteristicmay indicate whether a control that effects the temperature at which aprocess step occurs may indicate whether the temperature needs to beraised or lowered and/or the magnitude (e.g., in degrees) such a changeshould involve.

The response characteristic identified from the vibrational data may beany data or combination of data that is correlated to the processvariable. For instance, the vibrational data may comprise resonanceresponse data and the response characteristic may comprise a resonancemetric. That is, the response characteristic may comprise any value,relative values, statistical information, mathematical operation result,or any other appropriate manipulation of the vibrational data. Moreover,it may be appreciated that a plurality of response characteristics maybe correlated to corresponding different process variables. In thisregard, a plurality of response characteristics may be monitored suchthat any given one or more response characteristics that indicate anadjustment is needed for a control of a process variable may beidentified from the vibrational data.

Moreover, the process variable that is correlated to the responsecharacteristic may comprise any appropriate process variable for a partproduction process. Examples of such process variables may include amanufacturing variable that relates to the machinery or equipment usedto produce the part and/or any processing related thereto. For example,a manufacturing variable may comprise, but is not limited to, a processtemperature, a process rate, manufacturing component wear (e.g., diewear or the like), raw material properties, or any other variablerelated to the manufacture of the part. Further still, the processvariable may comprise a component variable. The component variable mayrelate to the resulting part or any part intermediary that affects thefinal part. For example, the component variable may comprise, but is notlimited to, a part dimension, a stress state, a crystallographicorientation, a material property, phase ratios, part chemistry, partmicrostructure, or any other variable related to the component. It maybe appreciated that a component variable may result from any one or moredifferent manufacturing variables. In this regard, in the case where theprocess variable comprises a component variable that is correlated tothe response characteristic, identification of the responsecharacteristic may allow for determination of a single adjustment or aplurality of adjustments to be made to appropriate controls for theprocess variable. That is, identification of a response characteristicin the vibrational data regarding a process variable may result in aplurality of controls of the part production process being implicated inan adjustment.

A response characteristic may be identified in relation to vibrationalresults for a single part or may be identified based on a change invibrational data between a plurality of parts. In the case ofidentification of a response characteristic in a single part, it may bethat the response characteristic must be observed in each individualpart over some number of parts. For instance, if a single part isproduced that includes a response characteristic that indicates aprocess variable is in a state that needs correcting, the process maynot be modified. However, if the response characteristic indicating theprocess variable is in a state that needs correcting occurs in a givennumber of parts or over a given time period, the control for the processvariable may be adjusted. The adjustment may be according to a givenresponse characteristic (e.g., the last part that had a responsecharacteristic indicating the change is needed) or may be according tosome representation of all parts with the response characteristicindicating the change is needed.

The definitions of a given number of parts and a given time periodprovided above in relation to batch size are equally applicable in thiscontext of a given number of parts or a given time period. The responsecharacteristic may be identified in response to a change in thevibrational data between a plurality of parts. In this regard, thechange in the vibrational data between the plurality of parts mayinclude a trend in the vibrational data. Such a trend may comprise aresponse characteristic indicating a change in the corresponding processvariable to the response characteristic is needed. To illustrateidentification of a response characteristic relative to a change in thevibrational data between a plurality of parts, FIG. 7 depicts a plot 230of vibrational data 232 for a plurality of parts. The vibrational data232 comprises plot points representative of the vibrational data 232 foreach one of a plurality of parts with the vertical axis representing thevalue of the vibrational data and the horizontal axis representing time.

In FIG. 7, each data point of vibrational data 232 may contribute to astatistical representation of the vibrational data. For instance, amoving average 234 may be represented as a line relative to thevibrational data 232. The moving average 234 may be updated with eachnew data point of vibrational data 232. While a moving average 234 isdiscussed, it may be appreciated that other statistical representationsmay be utilized in lieu of a moving average and the discussion of amoving average is for illustrative purposed only. In any regard, themoving average 234 may correspond to an average vibrational data valuegenerated using a given number of previous data points of vibrationaldata 232 corresponding to parts. The moving average 234 may includeweighting more recent parts relative to older parts.

In any regard, the moving average 234 may be monitored relative to anumber of values. For instance, an upper limit 238 and/or a lower limit246 are represented in the plot 230. If the moving average 234 crossesone of the upper limit 238 or lower limit 246, a response characteristicmay be identified that indicates a state of a process variable requiresadjustment. In this regard, the moving average 234 may allow foridentification of a trend 236 in the vibrational data 232 as representedby the portion of the moving average 234 that begins to deviate to arelatively steady state portion of the moving average to the left sideof the plot 230. It may be appreciated that the trend 236 may beidentified relative to limit 238 or limit 246 as described above oralternative measures of a trend. For example, continued movement of themoving average 234 in a single direction (e.g., either continued lowervalues or continued higher values of the vibrational data) over a givennumber of parts or for a given time period may also be used to identifya trend 236. For instance, the evaluation of vibrational data 232 maygenerally be over the course of a batch of parts. In this regard, any ofthe foregoing possible definitions of a batch of parts may be used todetermine the number of parts and/or time period over which parts areevaluated to determine a trend.

In any regard, it may be appreciated that an upper sort boundary 242 anda lower sort boundary 244 may also be represented in the plot 230. Theupper sort boundary 242 and the lower sort boundary 244 may representvibrational data values that, if a vibrational data value fails to fallbetween, results in the part being categorized in a non-compliant partclassification. That is, if the vibrational data 232 for a part fallsoutside a region bounded by the upper sort boundary 242 or the lowersort boundary 244, the part may be non-compliant and may be classifiedas such. In turn, identification of the trend 236 may occur prior to themoving average 234 falling outside the bounded area defined by the uppersort boundary 242 or the lower sort boundary 244. For instance, theupper limit 238 and the lower limit 246 may be defined relative to theupper sort boundary 242 and the lower sort boundary 244 such that if themoving average 234 crosses the upper limit 238 or lower limit 244, aresponse characteristic may be identified to allow for adjustment of acontrol for a process variable. This adjustment may occur prior to themoving average 234 crossing the upper sort boundary 242 or the lowersort boundary 244. In turn, the adjustment to the control for theprocess variable in response to the response characteristic beingidentified prior to the moving average 234 crossing the upper sortboundary 242 or the lower sort boundary 244 may reduce the likelihoodthat parts of a non-compliant part classification (e.g., a statisticallysignificant number of parts) are produced by the part productionprocess. In some instances, the adjustment of the control may preventany (or a significant number of) parts of a non-compliant partclassification being produced.

As shown in FIG. 1, the correlation analysis module 8 may be inoperative communication with the vibrational testing system 6 and thepart production system 2. The correlation analysis module 8 may comprisea memory that stores one or more correlations between a responsecharacteristic and a process variable. In this regard, the vibrationaltesting system 6 may access the correlation analysis module 8 toretrieve the correlations when identifying a response characteristicfrom the vibrational data and determining a state of a process variablecorrelated to the response characteristic.

In some embodiments, a plurality of metrics in the vibrational data maybe monitored for identification of a response characteristic in one ormore of the metrics. For instance, more than one response characteristicmay be identified from the vibrational data that indicates that morethan one process variable is to be adjusted. For instance, separate,potentially unrelated, process variables may be correlated to differentresponse characteristics that may each be uniquely identified whenanalyzing the vibrational data. These different response characteristicsmay relate to different portions or metrics of the vibrational data.Also, it may be that certain response characteristics may be identifiedfrom a batch analysis regarding collective vibrational data of aplurality of parts, while other response characteristics may beidentified from vibrational data of individual ones of the parts. Inthis regard, a batch characteristic may be identified that is correlatedto a first process variable based on an analysis of the collectivevibrational data for a batch of parts comprising a plurality of parts.Further still, a part response characteristic may be identified from thevibrational data of the individual ones of the parts that is correlatedto a second process variable that may be different than the firstprocess variable correlated to the batch characteristic identified fromthe collective vibrational data.

In addition, the correlation analysis module 8 may operate to identify acorrelation between a response characteristic and a process variable.Specifically, the correlation analysis module 8 may receive vibrationaldata regarding parts 4 from the vibrational testing system 6 and may getvalues for a process variable used to produce each corresponding part 4for which vibrational data is received. The process variable for aplurality of parts may differ for different ones of the parts 4. In thisregard, the correlation analysis module 8 may be operative to identify aresponse characteristic based on an analysis of the vibrational data ofa plurality of parts 4 for which differing process variable values areknown.

The correlation between a response characteristic and a process variablemay be determined based on an analysis of a multidimensional data set.The multidimensional data set may comprise a plurality of dimensionscorresponding to one or more metrics from the vibrational data (e.g.,vibrational dimensions). The multidimensional data set may also includea dimension corresponding to the process variable that varies over theplurality of parts 4 for which vibrational data has been obtained (e.g.,a non-vibrational dimension). In turn, the multidimensional data set maybe analyzed to determine which of the vibrational dimensions relate tothe non-vibrational dimension to determine a correlation therebetween.That is, the metric corresponding to the vibrational dimension thatcorrelates with the non-vibrational dimension may be identified ascorrelating to the process variable that comprises data of thenon-vibrational dimension.

In order to determine which of the vibrational dimensions correlates tothe non-vibrational dimension, at least one of a classification analysisor a regression analysis may be performed. For instance, theclassification analysis may include a classification in which thenon-vibrational dimension is classified in relation to the vibrationaldimensions to determine which of the vibrational dimension is mostrepresentative of the non-vibrational dimension. In this regard,classification of the non-vibrational dimension values into a givenvibrational dimension may be indicative that the vibrational dimensionis correlated to the non-vibrational dimension such that the responsecharacteristic corresponding to the vibrational dimension may becorrelated to the process variable. In a similar regard, a regressionanalysis may be used to determine which of the vibrational dimensionsmost closely correlates with the non-vibration dimension by determiningwhich of the vibrational dimensions most closely fits thenon-vibrational dimensions. Examples of potential evaluations mayinclude a non-linear least squares regression, a correlation coefficientanalysis, an analysis of variables (ANOVA) approach, a k-meansclustering approach, a principle components analysis, or a random forestanalysis. In any regard, once a correlation has been identified, thecorrelation may be stored for access by the vibrational testing system 6when attempting to identify a response characteristic and in turndetermining what process variable is correlated thereto. A plurality ofsuch correlations may be provided such that a plurality of metrics maybe monitored to determine a plurality of correlated process variables inthe analysis. It may also be appreciated that the correlation analysisby the correlation analysis module 8 may provide a measure of thedirection and/or magnitude of a process variable response based on thevibrational data.

A part production process may be modified from a default part productionprocess to a test part production process. The test part productionprocess may include control of one or more process variables to deviatethe one or more process variables from a default value. The testproduction part process may facilitate a number of important aspectsrelated to the disclosure presented herein. For instance, a test partproduction process may be used in connection with determining acorrelation between a response characteristic and a process variable. Inconnection with the process for determining a correlation betweenvibrational data and a process variable, it may be appreciated thatvarying values of the process variable of interest may assist in thedetermination of a correlation to the vibrational data being analyzed.

As described above, the correlation analysis module 8 may be inoperative communication with the part production system 2 to obtain avalue for a process variable used to produce a part having vibrationaldata that is analyzed. In this regard, the test production part processmay allow for changing a process variable in a controlled manner toproduce test parts. As the correlation analysis module 8 may be inoperative communication with the part production system 2, the processvariable for each test part produced by the test part production processmay be communicated to the correlation analysis module 8. As the processvariable is controllably varied in the test part production process, aplurality of test parts that each have a unique process variableassociated therewith may be tested to obtain vibrational data. Asdescribed above, the process variable may correspond to anon-vibrational dimension in a multi-dimension data set that is analyzedto determine a correlation between the response characteristic and theprocess variable. By employing the test part production process tointentionally vary the one or more process variables in a manner thatprovides varying values for the process variable to allow fordetermination of a correlation in the vibrational data over test partsproduced by the test part production process.

An embodiment to a method 250 related to such a test part productionprocess is shown in FIG. 8. The method 250 may include adjusting 252 acontrol for a process variable from default part production process. Theadjusting 252 of the control may result in an altered part productionprocess, thus resulting in the test part production process. The method250 may also include producing 254 test parts using the test partproduction process. After producing 254 test parts, it may be determinedat 280 whether all process variables for which test parts are desiredhave been used. If not all process variables have been used, the method250 may iterate to further facilitate adjusting 252 the part productionprocess for the next process variable desired. If it is determined at280 that all process variables have been used to product test parts, themethod may progress and include acquiring 256 vibrational data for thetest parts produced. In this regard, the method 250 may allow for aplurality of values for a process variable to be used in various testpart production processes so that test parts produced using differentprocess variables may be produced. This may be beneficial in determininga correlation between a process variable and a response characteristicby providing a range of process variables to allow for discernment ofthe resulting vibrational response resulting from the different processvariables.

The method 250 may include generating 258 a plurality of vibrationalmetrics from the vibrational data for the test parts. As describedabove, the vibrational metric may include any absolute value, relativevalue, mathematical operation, statistical representation, or otherappropriate manipulation of the vibrational data to produce the metric.In turn, the method 250 may also include generating 260 amultidimensional data set. The multidimensional data set may includevibrational dimensions corresponding to the vibrational data and/or thevibrational metrics generated at 258. The multidimensional data setfurther includes a non-vibrational dimension corresponding to thedifferent process variable used to produce the test parts, wherein thevalues of the non-vibrational dimension is provided in relation to thevibrational data for corresponding test parts produced using a givenvalue of the process variable.

In turn, the method 250 may include analyzing 270 the vibrationaldimensions relative to the non-vibrational dimensions. As describedabove, this may include approaches that include regression analysis orclustering to identify relationships or correlations between thevibrational data and the process variable values. One particularapproach may include use of Mahalanobis Taguchi System (MTS) math onvarious combinations of vibrational metrics and process variable values.MTS math is a central-tendency kind of analysis that calculates the‘distance’ of a value in a first dimension from the center of areference population resulting in a value called the MahalanobisDistance (MD). The MD is low when the value in the first dimension isnear to the center of the reference population (e.g., is highlycorrelated or similar), and high when the part is ‘not like’ thereference population (e.g., is not correlated or dissimilar). A biasvalue may also be calculated that is the ratio of the distance from thecenter of a first reference population to the center of a secondreference population. The bias value is high when the part is muchfarther from the first reference population than from the secondreference population, providing quantifiable analysis of the relativecorrelation or similarity between the respective reference populations.As such, a genetic algorithm may be used to adjust which combinations ofvibrational metrics are used, based on maximizing a score correspondingto the “correctness of sorting” or correlation value. For example, ifthe vibrational data and process variables using hypotheticalvibrational metrics numbered 1, 2, and 3 generate a score of 0.995, andthe same score for vibrational metrics numbered 8, 9, 10 is 0.85,vibrational metrics 1, 2, and 3 will be used more in futurecombinations, until some convergence on the score or correlation valueis reached. Vibrational metrics 8, 9, 10 will be used less frequently incombinations, because their score tended to be lower.

In turn, the analyzing 270 may include maximizing the correlation of theMD for a given vibrational dimension in a plurality of vibrationaldimensions to the non-frequency dimension associated with a varyingprocess variable. As described above, the process variable correspondingto the non-vibrational dimension may include, but is not limited to, apart dimension, the part mass, or a ‘real number’ type of valueassociated with the part (maximum temperature exposure, crystallographicorientation, creep percentage, or other value). When this analyzing 270is performed, the genetic algorithm described above may be used in asimilar manner, to maximize the correlation of MD between thevibrational metrics and the process variable.

In addition, a clustering analysis (e.g., k-means clustering) may alsobe used for a finite number of process variables, such as those attachedto discrete process variables such as “dies” or “cavities” used toproduce parts. The analyzing 270 may include optimizing k-meansclustering on vibrational metrics to calculate the center of eachdie/cavity cluster of vibrational data. In addition, a genetic algorithmmay be used to highlight patterns that maximize the distance betweeneach cluster, or minimize the overlap of clusters, such that the resultswere as ‘separate’ of clusters as could be obtained. The vibrationalmetrics that gave the most separate clusters would be ‘best correlated’to that process variable. Regardless of the analysis utilized, themethod 250 may also include identifying 272 a correlation between avibrational dimension and the non-vibrational dimension (e.g., bydetermining the highest or maximized correlation value or most separatedclusters).

Additionally or alternatively, a test part production process may beused to evaluate an intentional change in a process variable of adefault part production process. A method 300 is depicted in FIG. 9 thatmay be used for such evaluation of an intentional process change. Forinstance, the method 300 may include modifying 302 the default partproduction process such that one or more process variables isintentionally modified from a default value associate with the defaultpart production process to define a test part production process withthe modified process variable. In turn, test parts may be produced 304using the test part production process having the altered one or moreprocess variable. The test parts may undergo vibrational testing toacquire 308 vibrational data for the test parts. In addition, the method300 may include acquiring 306 data from qualification parts. Thequalification part data and the vibrational data for the test parts maybe compared 310. By way of example, the comparing 310 may includetesting the test parts against a sort (e.g., a part sort or a batchsort) to categorize the parts into one of a compliant partclassification or a non-compliant part classification. In turn, the testpart production process may be evaluated 312 in relation to thecomparing 310. Specifically, the evaluating 312 of the test parts mayinclude analysis of a response characteristic corresponding to theintentionally modified process variable to determine if thecorresponding frequency response of the response characteristic resultsin classification of parts into a non-compliant part classification orto determine the results of a sort specific to the responsecharacteristic. Again, this may include testing individual ones of thetest parts or a batch analysis on the test parts.

With further reference to FIG. 10, an embodiment of a method 350 isdepicted that includes aspects of the disclosure presented above. Whilein FIG. 10, both process feedback generation and part evaluation areperformed in relation to vibrational data, it may be appreciated thatboth aspects need not be performed in conjunction in all embodiments. Inany regard, the method 350 includes acquiring 352 vibrational dataregarding at least one part. As described above, the acquiring 352 mayinclude accessing a data store of previously obtained vibrational datafor a part or may include physically acquiring vibrational data throughactive vibrational testing of the part.

The method 350 may include part evaluation by testing 354 the partagainst a sort. In response, the part may be categorized 356 into one ofa non-compliant part classification or a compliant part classification.Such part evaluation comprising testing 354 and categorizing 356 may beperformed independently of any process control feedback.

However, the method 350 may also include identifying 358 a responsecharacteristic from the vibrational data. As described in detail above,the response characteristic may correspond to a vibrational metric fromthe vibrational data and may be correlated to a process variable of apart production process used to produce the part being tested. In turn,the method 350 may include determining 360 a state of the processvariable correlated to the identified 358 response characteristic. Asdescribed above, once the response characteristic is identified 358, afeature or attribute of the response characteristic may inform the stateof the process variable (e.g. providing an indication of a direction andmagnitude by which the process variable should be adjusted to achieve anoptimum or predefined value for the process variable). In this regard,the determining 360 may include determining an offset or differentialbetween the state of the process variable as determined 360 from theidentified response characteristic to an optimum or predefined value forthe process variable. Such analysis may be used to generate 362 dataregarding an adjustment to the process variable that is determined basedon the state of the process variable determined at 360. Further still,the method 350 may include actually adjusting 364 the process variableusing the data regarding the adjustment.

FIG. 11 presents another embodiment of a method 400 that may utilize abatch approach to part evaluation and process control feedback. Asstated in relation to FIG. 10, while shown used in combination in FIG.11, such aspects of the present disclosure need not be used inconjunction and could be performed independently. In any regard, themethod 400 may include identifying 402 a batch of parts. As describedabove, the identifying 402 may include identifying a batch of partsproduced in a true batch process, or collecting a given number of partsproduced or collecting parts produced over a given time period. Themethod 400 further includes acquiring 404 vibrational data for each ofthe parts of the batch.

The method 400 may also include generating 406 collective vibrationaldata for the plurality or parts. As described above, this may include astatistical representation of the vibrational data for the individualones of the parts in a batch and/or may include discounted values forthe vibrational data for certain ones of the individual parts (e.g.,based on an evaluation of individual parts as described in relation toFIG. 10). The method 408 may further include comparing 408 thecollective vibrational data to a batch sort. The batch sort may includea batch threshold such that the comparing 408 may include determiningwhether the collective vibrational data for a given batch satisfies thebatch threshold. The batch threshold may be evaluated in relation to asingle given batch of parts or may include parameters related to thechange in collective vibrational data between given batches. In anyregard, the method 400 may include determining 410 whether the batchsatisfies the batch threshold. The method 400 may also include updating420 the batch status based on the determining (e.g., to classify thepart in the batch into a non-compliant classification as a result of thebatch threshold not being satisfied or classifying the parts for furtherevaluation).

The method 400 may also include generation and/or use of processfeedback based on the collective vibrational data. In this regard, themethod 400 may include identifying 412 a response characteristic fromthe collective vibrational data. The response characteristic may beidentified from the collective vibrational data from a single givenbatch or may be identified from a change in collective vibrational databetween given batches of parts. In any regard, the responsecharacteristic may allow for determining 414 the state of a processvariable in the manner described above in relation to FIG. 10. Moreover,the response characteristic identified from the collective vibrationaldata for the batch of parts may be different than that of the individualparts and may be correlated to a different process variable. In anyregard, the method 400 may include generating 416 data regarding anadjustment to a control of the part production process based on thestate of the variable determined at 414. Furthermore, the method 400, inat least one embodiment, may include adjusting 418 the control of theprocess variable.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and description isto be considered as exemplary and not restrictive in character. Forexample, certain embodiments described hereinabove may be combinablewith other described embodiments and/or arranged in other ways (e.g.,process elements may be performed in other sequences). Accordingly, itshould be understood that only the preferred embodiment and variantsthereof have been shown and described and that all changes andmodifications that come within the spirit of the invention are desiredto be protected.

What is claimed is:
 1. A method for generating a feedback adjustment forcontrol of a part production process, comprising: acquiring vibrationaldata for at least a first part produced by the part production process,wherein the vibrational data includes a frequency response of the firstpart when excited at a plurality of input frequencies; identifying aresponse characteristic of the first part from the vibrational data forthe first part, wherein the response characteristic is correlated to aprocess variable of the part production process that produced the firstpart; determining a state of the process variable of the part productionprocess that produced the first part based on the responsecharacteristic of the first part that is correlated to the processvariable; and generating a feedback adjustment for a control of theprocess variable of the part production process in response to the stateof the process variable.
 2. The method according to claim 1, wherein thevibrational data comprises resonance response data and the responsecharacteristic corresponds to a resonance metric based on the resonanceresponse data.
 3. The method according to claim 1, wherein the acquiringcomprises acquiring vibrational data for a plurality of parts producedby the part production process, and the response characteristic is atleast based on a change in the vibrational data between different onesof the plurality of parts.
 4. The method according to claim 3, whereinthe change in the vibrational data between different ones of theplurality of parts comprises a trend in the response characteristicmeasured over a given number of the plurality of parts relative to theorder in which the parts were produced.
 5. The method according to claim3, wherein the change in the vibrational data between differentrespective ones of the plurality of parts comprises a trend in theresponse characteristic measured over a given time period in which theplurality of parts are produced.
 6. The method according to claim 1,further comprising: testing the frequency response against a sort,wherein the sort is based upon vibrational data from a qualificationpopulation.
 7. The method according to claim 6, wherein the sortcomprises at least one limit relative to the frequency response definingat least one of a compliant part classification sort result or anon-compliant part classification sort result.
 8. The method accordingto claim 7, wherein the generating the adjustment for the control occursprior to a change in the vibrational data between ones of the pluralityof parts resulting in a non-compliant part classification sort resultfor a part of the plurality of parts produced by the part productionprocess.
 9. The method according to claim 1, wherein the state of theprocess variable is determined based on a determination that theresponse characteristic has exceeded a limit established in relation tothe response characteristic.
 10. The method according to claim 1,wherein the state of the process variable is determined based onidentification of a trend in the response characteristic.
 11. The methodaccording to claim 1, wherein the state of the process variable isindicative of a magnitude of adjustment of the control.
 12. The methodaccording to claim 1, wherein the state of the process variable isindicative of a direction of adjustment of the control.
 13. The methodaccording to claim 1, wherein the correlating further comprises:acquiring vibrational data for a plurality of parts produced by the partproduction process; obtaining respective process variable values for theprocess variable for each of the plurality of parts, wherein therespective process variable values differ with regard to the pluralityof parts; identifying a correlation between the process variable and thevibrational data including evaluating a multidimensional data set inwhich each of a plurality of vibrational metrics comprise correspondingrespective vibrational metric dimensions in the multidimensional dataset and the process variable comprises a non-vibrational dimension inthe multidimensional data set; and correlating the responsecharacteristic from the vibrational data to the process variable. 14.The method according to claim 13, wherein the identifying thecorrelation includes evaluating a plurality of vibrational metricdimensions relative to the non-vibrational dimension to determine thecorrelation between a given vibrational metric dimension and thenon-vibrational dimension.
 15. The method according to claim 14, whereinthe evaluating comprises at least one of a non-linear least squaresregression, a correlation coefficient analysis, an analysis of variance(ANOVA), k-means clustering, principal components analysis, or randomforest analysis.
 16. The method according to claim 1, furthercomprising: exciting the first part at the plurality of inputfrequencies; measuring the frequency response of the first part; andgenerating the vibrational data for the first part based on the measuredfrequency response of the first part.
 17. The method according to claim16, wherein the process variable is a manufacturing variable comprisingat least one of a process temperature, a process rate, manufacturingcomponent wear, or a raw material property.
 18. The method according toclaim 16, wherein the process variable relates to a component variablecomprising at least one of a part dimension, a stress state, acrystallographic orientation, a material property, phase ratios, partchemistry, or part microstructure.
 19. The method according to claim 1,further comprising: adjusting the control of the part productionprocess.
 20. A tool for generating a feedback adjustment for control ofa part production system that performs a part production process,comprising: a data store comprising vibrational data for at least afirst part produced by the part production process, wherein thevibrational data includes a frequency response of the first part whenexcited at a plurality of input frequencies; a vibrational testingsystem in operative communication with the data store to retrieve thevibrational data, wherein the vibrational testing system is operative toidentify a response characteristic of the first part from thevibrational data for the first part that is correlated to a processvariable of the part production process that produced the first part anddetermine a state of the process variable of the part production processthat produced the first part based on the response characteristic of thefirst part that is correlated to the process variable; and a controlmodule that is in operative communication with a control of the partproduction process that controls the process variable, wherein thecontrol module is operative to generate a feedback adjustment for thecontrol of the part production process variable in response to the stateof the process variable.